Thierry Chevaillier

French academics: Between the professions and the civil service

In France, where most higher education institutions are in the public sector, permanent academic staff at universities and other higher education institutions are tenured and enjoy a broad individual autonomy. The number of staff on shortterm contracts is limited. Most part-time staff are drawn from professionals, business executives, and administrators -in both the public and private sectors. In recent decades, the number of teaching-only faculty expanded rapidly as tenured secondary school teachers were transferred to higher education institutions. As universities have acquired some autonomy, especially in academic matters, recruitment and promotion of staff combine elements of centralisation at the national level with local processes. Some degree of pay differentiation has been introduced through a growing use of supplementary compensation. As the system evolves toward increased self-governance of institutions, the autonomy enjoyed by the faculty is seen by administrators as an obstacle to the development of institutional policies.

French higher education is often viewed from abroad as an oddity. Most of its peculiar features are rooted in a long history of centralisation, in the hands of the state, of decision-making power over almost every aspect of social life. Some of the directions taken during almost two centuries were radically different from those of other European countries. The resulting system has been a model for a number of countries -such as the francophone African countries, which at the time of independence had inherited elements of a higher education system created by the former colonial power. Owing to their cultural proximity to France, Belgium, Italy, and to a lesser extent Portugal and Spain have also been influenced by the French model in some aspects of the organisation of their higher education systems. Similarities that might be discerned in the structure adopted in the Soviet Union for higher education and research did not have much to do with imitation but could better be viewed as "objectively" determined by the choice of a centralised pattern of organisation for the whole society. Since the early 19th century, the higher education system has been mostly in the public sector. Although institutional autonomy has increased in the recent period, it is fairly limited, compared with other European countries. Control of higher education institutions is distributed among various ministries of the central government, with little involvement of local authorities. The Ministry of Education controls and funds the universities and a large number of other public higher education institutions. Other ministries control and fund similar institutions, which are often more prestigious than universities. The private sector is small and, apart from a few Catholic universities, consists only of independent schools of engineering and business administration. The most peculiar feature of higher education is the existence, outside the universities, of a strong public research sector and of an "elite" sector of vocationally oriented specialised institutions. In public higher education institutions, most permanent staff are tenured and enjoy the status of public employees. In the general framework of the Public Employee Statute (Statut général de la fonction publique), they belong to a number of different categories of public employees, each category (called corps) with its own regulations covering recruitment and promotion and its own job definition and responsibilities. In universities, academic staff historically used to be highly stratified in many different categories. During the last two decades, a reform of the statutory framework and an evolution in the practices of the institutions has brought about a more unified structure for the academic profession.

Overview of the system

In France, what is defined as higher education comprises short vocationally oriented programmes in universities, secondary schools, and independent institutions, as well as longer and more general programmes offered by universities and independent grandes écoles. This situation stems from a long tradition of creating a new institution whenever new needs arise. Even before the abolition of the universities during the French Revolution, the state organised the training of the necessary qualified manpower by establishing specialised institutions. Universities -in the usual sense of autonomous, comprehensive institutions -appeared only in the second half of the 20th century and were allowed to offer programmes in most fields of higher education in the following decades. Today, the higher education sector includes a number of different categories of institutions:

• public-sector universities (87) -including 3 national polytechnic institutes and 3 technology universities. They differ widely in size, ranging from 1,000 to 40,000 students, with an average of 15,000. Altogether, they enrol about 1,400,000 students (in 1998). They offer a large variety of programmes: short vocational "technician" programmes in IUTs, programmes in engineering and in business administration as well as traditional studies in the natural sciences, social sciences, arts and humanities, medicine and law.

• teacher training university institutes (instituts universitaires de formation des maîtres, IUFM) (28) -formally independent but closely related to universities, with a total enrolment of more than 80,000 students.

• private (mostly Catholic) universities (5) and colleges ( 14) -enrolling less than 1 percent of the students (about 22,000 students).

• higher schools (écoles supérieures, the most prestigious being referred to as grandes écoles) (ca. 500) -they can be private, public or of mixed status like institutions created and funded by local chambers of commerce. They tend to specialise in a few fields that were traditionally not offered in universities: engineering, agriculture, business administration, architecture. They are much smaller than universities: in engineering schools, for example, enrolments range from less than 100 to 3,500, with an average size of 300 students.

• fine arts and applied arts schools -these are controlled by the Ministry for Cultural Affairs and operated by the state or by local authorities.

• independent institutions -these offer shorter programmes in health and social services (training of nurses, midwives, social workers, special education staff, etc.). Most of these schools are controlled by the Ministry for Health and Social Affairs.

In France, the higher education system includes two types of programmes organised and taught in upper secondary schools (lycées), either public or private, by secondary school teachers:

• Special preparatory programmes leading to the grandes écoles are usually highly selective. These classes (préparatoires aux grandes écoles) constitute the traditional first stage of study leading toward engineering or business schools, the elite track in French higher education. They enrol nearly 80,000 students.

• Short vocational programmes, similar to IUTs, called STS (sections de techniciens supérieurs) and leading to a BTS, or Higher Technician Diploma (Brevet de techniciens supérieurs), with about 230,000 students. Postgraduate studies and doctoral programmes are offered only in the public universities. Nevertheless, the grandes écoles increasingly seek agreements with universities to take part in postgraduate programmes.

The structure of the profession

Historical perspective

The present situation of academic staff should be put briefly into historical perspective in order to trace the peculiarity of the French traditions and the variety of recruitment practices and careers among the different disciplines. Since the early 1800s, when Napoleon reconstructed the higher education institutions abolished during the French revolution, until the end of the 19th century, there was one institution only -initially named the Imperial University. It comprised both secondary and higher education. In the largest cities there were local branches of the University, called faculties, in charge of higher education. They were managed from Paris by the minister of education, whose title was "Grand Master of the University". Staff were recruited nationally and allocated to these faculties. There were five types of faculties: theology, law, medicine, arts, and science. In the 20th century until the creation of autonomous universities in 1968, the faculties remained the basic administrative unit of the university sector of public higher education. Among the different faculties and often within the same faculty, in the various disciplines, careers used to follow different patterns. In the arts and science faculties, the main purpose was to train secondary school teachers. Graduates were recruited by the state through competitive exams and started their careers in secondary schools. Later they transferred to faculties where they would assist professors until they replaced them after spending many years writing a doctoral thesis. The medicine and law faculties trained medical practitioners and lawyers or judges respectively. Permanent teaching staff were recruited initially from an older pool of practitioners working part time in the faculties. For them the doctoral thesis was a less ambitious under-taking and written in a much shorter period of time. These are examples of the differences in recruitment practices in the disciplines -which persisted even after the law was changed in 1984, with a new statute providing for common regulation with regard to academic staff in most of the disciplines. The rapid expansion of universities in the 1960s and the 1970s was made possible by employing a large number of young assistants on part-time or fixed-term contracts, only a minority of whom could expect to become permanent academic staff. The existing career patterns, which were created in a higher education system with just a few thousand students and a few hundred professors and assistants, were not workable in a mass system that would soon reach a million students. A complete overhaul of the training and recruitment methods of the profession was called for. In the wake of the 1984 Higher Education Act, the government passed a series of decrees unifying doctoral studies, reorganising careers, and creating permanent positions for nonprofessorial staff. In the rest of higher education, there are still a variety of terms and conditions for academic staff. The various ministries and institutions have devised their own regulations and career structures, shaped by their technical or administrative circumstances. In the public sector as a whole, however, recruitment procedures and working conditions for academic staff are slowly converging, as a result of a process started by the 1984 act. Most of the institutions under the Education Ministry now recruit staff on the same conditions as universities, and other ministries are also moving in the same direction. The Agriculture Ministry, for example, has recently altered the organisation of its higher education institutions and basically adopted the regulations and procedures set by the Ministry of Education.

In the following discussion we will be limited to the public higher education sector which is controlled by the Education Ministry and employs the overwhelming majority of the country's academic staff. While concentrating on the public sector, the article will also make reference to the rest of higher education. The academic profession comprises three broad categories of positions: (1) tenured teaching staff of two different types -"teacher-researchers" (enseignants-chercheurs), and "teaching only" academic staff; (2) full-time or part-time staff on fixed-term nontenured contracts -either young academics waiting for appointment to tenured positions or specialists; (3) and staff employed on an hourly basis.

Tenured full-time positions Higher education personnel Staff with higher education status included in 1998 nearly 18,000 professors (professeurs des universités), about 30,000 lecturers (maîtres de conférences), and 1,650 tenured assistants (assistants titulaires). Assistants, initially appointed for limited terms, fought for tenure in the 1970s and succeeded in the early 1980s. The 1984 higher education statute called for the phasing out of this category. No new tenured assistant positions have been filled since 1984, and positions are closed when they become vacant. Their number has dwindled from about 15,000 in the late 1970s to less than 2,000 at present.

Secondary education teachers

Staff mobility from upper-secondary education to higher education has always been common since the Imperial University included both levels of education. However, it is only recently that the permanent assignment of secondary education staff became an important feature in higher education. It started with the creation of IUTs in 1966 and up until the mid-1980s it was limited to around 4,000. From 1986 on, their number grew steadily to the current figure of more than 12,000. Although these staff members are not recruited in the same way as higher education tenured staff, their careers are organised along similar principles -since they are public servants employed by the same ministry. There are two types of secondary teachers, professeurs agrégés and professeurs certifiés. Traditionally, in some disciplines (arts, humanities, sciences), academic staff were recruited from the ranks of secondary education teachers. The typical career path of academics in those disciplines was as follows: the baccalaureate, then a few years in a preparatory programme at one of the top secondary schools (lycées), école normale supérieure (elite teacher training institution), then the Agrégation (competitive examination for recruiting secondary education teachers), then a period in a tenured teaching position in a secondary school, then appointment to a university as a tenured assistant, and, finally, recruitment as a lecturer or a professor. This pattern has changed over the last decades: a large number of secondary school teachers are now assigned to universities without any change in their status. They are likely to remain secondary school teachers and to keep the same teaching load and the same career structure. They are designated "teaching only" staff as opposed to staff with university status, who are called "teacherresearchers" in the administrative jargon of French education. They may request a return to secondary schools when they wish. Some of them try to change their status, applying for lecturer positions after taking a Ph.D. -which is a difficult task owing to their heavy teaching load. Universities may now reduce the teaching load or grant research leave for staff engaged in a doctoral programme, to enable them to complete their Ph.D. dissertation. However, a few graduates recruited as secondary education teachers actually never teach in secondary schools but, rather, enter Ph.D. programmes (with grants) immediately after their qualification as secondary education teachers. They are rapidly recruited for temporary research and teaching positions, which are the first steps in an academic career at higher education institutions. Most of them have studied at one of the three écoles normales supérieures, elite institutions created in the early 19th century to train secondary education teachers.

Tenured secondary education staff who make up 10 percent of the total teaching staff at universities comprise 40 percent of staff in the short vocational programmes and 25 percent in the humanities departments. The increase in the number of teaching-only staff is one of the most notice-able trends in academic appointments in the last decades. They are mainly employed in vocational programmes and for "nonspecialist" teaching (e.g., teaching of foreign languages outside language departments or mathematics outside mathematics departments), where there is little need to link teaching with research work. This is precisely the type of programme and the sort of teaching that has been on the rise over the period. Moreover, their teaching load is twice that of lecturers, which lowers the instructional costs. Finally, their transfer to higher education was the easy policy option for the education authorities when a demographic downturn released teachers from secondary education at a time when higher education was still expanding rapidly. See Table 1, for the number of tenured academic staff in 1985 and 1998.

Staff on fixed-term contracts

Associate or visiting staff

Universities are allowed to use vacant permanent positions (for either professor or lecturer) to recruit either full-time associate staff (personnel associé) on three-year contracts or visiting staff (personnel invité). In recent years, a new type of contract has been introduced that enables universities to recruit as part-time associates people already employed in the public or the private sector -mainly among professionals, business executives, administrators, or researchers. Visiting professors usually are foreign academics invited for a few months by universities. Associate and visiting staff (who number about 2,500) have the same academic duties and rights as tenured staff.

Temporary assistants

Temporary assistants (attaché temporaire d'enseignement et de recherche -ATER), who number 5,300, are advanced students nearing the completion of their Ph.D.s or having just completed it, who are hired on a part-time or full-time basis for a limited term of one year (renewable twice, with the exceptional fourth year). This position is conceived of as a way of bridging the gap between the termination of the Ph.D. grant and recruitment for a tenured position. Universities can appoint these assistants to fill temporarily vacant tenured staff positions.

Instructors

Instructors (moniteurs) are drawn from graduate students awarded a research grant (allocation de recherche). Institutions can select a few candidates who will be given a supplementary compensation in exchange for a small amount of teaching (60 hours per year). Their teaching is restricted to first-and second-year programmes. Moniteurs are assigned a "mentor" and are required to attend special courses organised in 14 regional Higher Education Teacher Training Centres (Centres d'initiation à l'enseignement supérieur -CIES). Research grants are awarded for two years, usually extended to a third year -90 percent of the grantees (allocataires) are funded for a third year. Among the 10,000 allocataires (an annual flow of about 3,500) 4,000 are moniteurs.

Foreign-language assistants

Foreign language assistants (lecteur et maître de langues étrangères) are appointed for a period ranging from one to six years. They should be native speakers and hold a degree equivalent to Maîtrise for lecteurs and a postgraduate degree for Maîtres de Langues. There are about 950 of these assistants.

University hospital teaching staff

University hospital teaching staff (enseignants hospitalo-universitaires contractuels) are jointly appointed by the head of the medical school and the university hospital. There are approximately 3,600 of such teaching staff.

Hourly part-time staff

Universities and grandes écoles employ teaching staff paid by the hour. They are named chargés d'enseignement vacataires and attachés temporaires vacataires. They are hired for one term and for a given number of teaching hours by the head of the institution, after consultation with the committee for recruitment in the relevant discipline. Chargés d'enseignement vacataires should be fully employed outside the university. Attachés temporaires vacataires must be doctoral students under 26 years of age. Together with permanent university staff working overtime, hourly part-time staff account for a sizable share of teaching. Institutions receive some recurrent funding for employing part-time personnel or for overtime, when permanent staff allocated to them cannot meet their teaching needs. Some disciplines and newly created or rapidly expanding institutions, rely heavily on part-time staff. A few years ago, some universities expended more than half the recurrent funding for instruction on part-time and overtime teaching. The recent drop in student enrolments along with the corresponding decrease in recurrent funding has greatly reduced the hiring of such part-time staff and the flexibility it offered to institutions.

Higher education and research

Most publicly funded research is organised by specialised research agencies or public corporations distinct from the universities. The best known of these agencies, the CNRS (National Centre for Scientific Research), employs about 26,000 people, as compared with the 80,000 employed by universities. CNRS used to run its own independent laboratories, but it increasingly works with universities in funding university laboratories and operating jointly controlled and financed "mixed research units". Eighty percent of the research units of the CNRS are located within universities.

Other research agencies, smaller and more specialised, operate basically in the same way as the CNRS. 1 Full-time research staff are usually employed by national research agencies like the CNRS, even when they work in university laboratories or institutes. Full-time tenured researchers may take part in teaching on a voluntary and part-time basis but not as part of their statutory duties. Researchers get extra financial compensation for such teaching -for which they must secure formal authorisation. They may also be involved in supervision of research students working in their research laboratories (without any compensation). They may be assigned (détachés) to universities for a few years, in which case they are subjected to the statutory regulation of tenured teachers before reverting to their research position. Although they usually hold the qualifications required to apply to academic positions in universities, they tend to remain at the research agencies. Out of 26,000 staff employed by CNRS, more than 11,000 are "researchers", the remaining 15,000 being either "technical" or "administrative" staff. There are two ranks for researchers: chargé de recherche (research fellow) and directeur de recherche (research director), broadly equivalent to the two grades of lecturers and professors in higher education institutions.

Research centres and laboratories also employ casual labour, research students, and young Ph.D.s, who assist permanent staff in research projects -especially short-term projects and contract research. They may be formally hired by universities or research agencies, and their jobs are very insecure -similar to that of teaching vacataires. Among the most-qualified research support staff (ingénieurs), some employees are very close to research or teaching staff in that they perform tasks that are broadly similar. Although they hold permanent positions and may take part in teaching, they are not considered academic staff.

Student/staff ratios

In recent years, student/staff ratios that had been increasing over the last three decades stabilised owing to a halt in the expansion of student enrolments while staff recruitment policies were maintained. This contrasts with the constant deterioration of the ratio of teaching to nonteaching staff that points to a worsening of the working conditions of academic staff. See Table 2, for a comparison of these ratios for 1985 and 1998. Student-teacher ratios vary by discipline and by institution. In 1996, the average student-teacher ratio was 49.2 to 1 for the law and economics sector, 35.9 to 1 for the humanities and social sciences sector, 17.2 to 1 for science and engineering, and 11.6 to 1 for medical studies. Among universities, the distribution of average student-teacher ratios across all disciplines ranged from 50 to 1 in Paris II, which specialises mainly in law, to 9.4 to 1 in Toulouse Polytechnic Institute, which offers only engineering programmes. Among similar institutions, like comprehensive universities, the range is smaller, with ratios from 20 to 1 for long-established institutions to 40 to 1 for newer overseas universities. When the university funding system was altered in the mid-1990s, evening out the ratios among comparable universities became a new element of resource allocation policy.

Gender distribution

Although the gender distribution is highly skewed in favour of men, in higher education the issue is just beginning to be taken seriously in France. In 1997, among tenured staff, women accounted for 13.7 percent of professors (compared with 9.1 percent in 1987) and 35.5 percent of lecturers (30.8 percent in 1987). The gender distribution varies by discipline and with age. As a rule, the higher the academic position, the more men are found. But the situation is changing fast, and the overall gender distribution of teaching staff is getting closer to that of the student population. Since there are presently more female than male students in French universities, the share of women among the academic staff will go on increasing. This is especially true of the arts and humanities, where women account for 70 to 80 percent of the student body. Quite interestingly, medicine and law, which not so long ago were considered "male" disciplines, are undergoing rapid change, with more female than male students graduating and entering the labour market. The share of female staff is still low in the sciences and in engineering, but this is also a sector where female students are a minority. Although women are underrepresented in the higher ranks, their share is steadily increasing -following the pattern in secondary education, where they are now a majority. This is certainly a consequence of the dramatic increase in the participation of women in the labour force, as well as of the fact that, in education perhaps even more than in other parts of the public sector, young women find it possible to combine a career and motherhood (see Table 3).

Selection and appointment of tenured staff

Like most top civil servants, tenured academic staff are recruited on a "national" basis. This means that they are appointed by a state official (the education minister or the president of the country, in the case of full professors) at the end of a precisely defined procedure called concours -in which all applicants are "screened" by a national panel, appointed by the minister. Public employment is regulated by different sets of rules for permanent tenured staff (under the provisions of the Public Employee Statute) and for temporary personnel (hired through "public law" contracts). Litigation related to both types of public employment is settled not in ordinary courts but in administrative courts (tribunaux administratifs) specifically designed to handle conflicts between individuals and the state or other public authorities.

Teaching positions of higher education status Every year, universities inform the ministry of their wishes concerning new and vacant positions: what type of staff they need, in which disciplines, and whether they would like to keep positions vacant for future recruitment (in which case they will receive funding to employ part-time or temporary staff). The number of positions they ask for usually exceeds by far the number provided by the education budget.

Within the overall limit of the number and type of positions approved yearly by Parliament, the education minister allocates positions to univer-sities. The ministry issues a list of positions for which concours will be organised, specifying the type, the institution, and the discipline. Concours are advertised in official state publications (Journal officiel de la république and the Bulletin officiel de l'education nationale). Before being opened for recruitment, these positions may be filled by tenured staff of the same category working in other universities, which is not considered to be recruitment but, rather, transfer (mutation) and only needs the agreement of the recruiting boards of the university. Upon appointment at a given university, one becomes a professor of the French universities (Professeur des universités) and, while one cannot be transferred against one's will to another institution, one can without difficulty move to another institution. In 1998, one-fifth of the open professorial positions in universities were filled through such transfers (the proportion rises to one-third in law and economics and to over one half in political science).

The organisation of the concours

Every concours ends with an appointment proposal made to the minister or the president of the republic; it is generally endorsed. In the regular recruitment procedure, there are two phases in the concours, one involving the institution and one involving a national selection panel -the National Council of Universities (Conseil national des universités, or CNU). Members of this council are in part elected by academics and in part appointed by the minister. The council is divided into groups and sections, each section being in charge of a specific disciplinary field. (There are 72 different sections, some of them being divided into subsections.) A typical section comprises 48 members, of which 16 are appointed and 32 elected.

The recent period has witnessed frequent changes with regard to whether the institution or the council would take precedence in the joint decision making of these two bodies. Either the institution could first short-list applicants, from whom the CNU would select the most-qualified candidates. Alternatively, the institution would choose its favourite candidate from a national list of applicants previously screened by the CNU. Concerns revolve around the fact that, in the one case, the CNU could make proposals that disregard the specific needs of an institution, whereas in the other case the institution could favour less-qualified local candidates.

In the present status of the recruitment process, the CNU first screens individual applicants for tenured academic staff positions. Candidates for a teaching position in a higher education institution apply to the CNU for "qualification". Every spring, a list of qualified candidates is drawn up in every academic discipline by the appropriate section of CNU. Qualification is valid for a limited period of time: if qualified candidates are not recruited during the four years following the CNU's decision, they have to apply again for qualification.

The other phase takes place in the institution. Each university operates recruitment committees (commissions de spécialistes) organised around the 70-odd disciplines making up the sections of the CNU. In small institutions, one committee may be organised for several related academic disciplines. Each committee is made up of from 10 to 20 elected members drawn from the institution's faculty and of members from other institutions or from other disciplines of the same institution. These committees, in which professors and lecturers sit in equal number, are elected for 4 years and meet as often as is necessary to deliberate on staff recruitment and promotion. Each member has a substitute in case he or she is unable to attend a meeting. During a given recruitment campaign, only those members who have taken part in all the successive sessions of the recruiting board (including interviews) are allowed to cast their vote at the end of the process. This rule was introduced to prevent members from taking part in the final decision without any knowledge of the candidates.

Only candidates "qualified" by the CNU may apply to vacant positions that are advertised yearly. They apply to a specific position in a specific insti-tution. Applications are processed by institutions in the early summer so that newly recruited staff can start teaching at the beginning of the fall semester.

In a few disciplines (law, public administration, political science, eco-nomics, and business studies) a different recruitment procedure (agrégation) is used for professors only. Candidates need not apply first to CNU for qualification and to individual institutions after qualification. Rather, single national recruiting board, appointed by the minister, deals with the recruitment of professors in each discipline. The selected candidates are allowed to choose the university to which they will be assigned, according to their ranking at the end of a series of competitive examinations. This peculiarity explains why so many vacant positions are filled by transfer rather than by recruitment in these disciplines: the lowest ranking of the newly recruited professors, who were not assigned to their first-choice university, subsequently try to move to another institution.

Qualifications of academic staff

The degrees and titles formally required for application are statutory and stated in the regulations governing recruitment. Full-time permanent staff, lecturers and professors, are required to hold a Ph.D. -except for associate staff, who may apply for tenure without a Ph.D. a few years after their recruitment. Since 1984, an extra qualification is required for professorial positions: the Habilitation à diriger les recherches (somewhat similar to the German Habilitation), which is obtained after submission and defense of a thesis or a collection of research papers or books. Contractual staff -moniteurs, attachés temporaires, and attachés temporaires vacataires -are recruited from doctoral students, who have obtained at least a DEA (Diplôme d'études approfondies), a one-year postgraduate research degree. Attachés temporaires must either have completed their Ph.D. or be within a year of its completion.

There is no formal requirement for part-time staff -vacataires, paid by the hour, and associés, on contracts -provided they are already employed. Institutions are forbidden to hire people who are unemployed or retired.

Teaching staff of secondary education status Initial recruitment of secondary education staff is conducted through nation-wide competitive examinations (concours national). Successful candidates are allocated to secondary schools according to the needs of the national education system and to a set of criteria, including their ranking at the end of the contest. Their recruitment in higher education is treated as a secondment, and there is no particular procedure for it. After being informed by the ministry that they have been allocated secondary teacher positions, the universities advertise the positions they need to fill and process the applications as they see fit. Some institutions refer the applications to a standing recruitment committee. Others organise ad hoc committees, designed for each specific recruitment. Once the choice is made, the university president officially asks the ministry to assign the selected candidate to the institution. There is no time limit to the secondment, and teachers may return to secondary schools if they wish. In exceptional situations, a university dissatisfied with a seconded teacher may ask the minister or the ministry's regional representative to send the teacher back to the secondary schools.

Tenured research positions

Public research staff are employed by the government in tenured positions authorised by Parliament and allocated by the Ministry for Research to the various national research agencies. Although tenured research staff are not employed by the higher education institutions, it is worthwhile mentioning a few features of their conditions of employment. First, a career in public research is the main alternative to a career in higher education for most Ph.D. graduates. Second, the number of transfers between research and teaching positions is not negligible. Career paths, are similar, including prerequisites for recruitment and pay structure, in public higher education and in public research. For this reason and because most research units of the national agencies work in close cooperation with university laboratories (indeed, more and more units are jointly owned by research agencies and universities), transfer between sectors is quite easy. In 1995, 1 in 20 lecturers and 1 in 10 professors newly recruited by universities came from the various research agencies. There have been recurrent plans by government to merge the two types of institutions and to end this peculiar arrangement of the French research establishment, but they have repeatedly failed. In recent years the stagnation in the number of research positions authorised by Parliament has increased the pressure on research personnel to apply for positions in universities, where the growth of academic positions has occurred.

Appointment of temporary and nontenured staff

Procedures for the recruitment of temporary teaching or research staff on an hourly basis (vacataires) are much less rigorous. Decisions are taken at the institutional level -in practice, at the departmental level -after consultation with the appropriate recruitment standing committee. The proportion of nontenured staff is higher in universities (about 20 percent) than in other institutions (6 to 9 percent). It is especially high in the medical schools (32 percent). Working conditions for these teaching or research staff on contract, working part time or full time, are more strictly regulated. As they are employed by a state institution, they are required to sign a specific type of labour contract (contrat de droit public) that differs from ordinary labour contracts. Such contracts are regulated by law, and no bargaining takes place between the parties.

To circumvent the rigidity of such contracts, the legal responsibility for employing temporary staff is sometimes transferred to private, foundation-like bodies, called associations. Such bodies are quite easy to set up under French law. This practice, although repeatedly condemned by the Court of Accounts, is common -but less so for teaching than for research.

Evaluation and promotion

Newly recruited academic staff, like most state employees, are granted tenure (they become titulaire) after a short one-or two-year period of probation. Once they are granted tenure, they benefit from the common public employment statute (Statut général de la fonction publique) and from the specific statute of their category, which guarantees them continued employment, sets their rights and duties, organises their career, and regulates their financial compensation.

Tenure

Periodically, after the numbers of contract staff have built up for some time, unions demand that some categories of nontenured staff be granted tenure without having to submit to the formal recruitment procedures, through a process called intégration. This happened in the past -for example, when nontenured assistants were given tenure en masse in the 1970s. Since then, the ministry has stated explicitly that nontenured positions are short-term ones that will never lead directly to tenured status; they have set age limits for the recruitment of part-time staff or required that applicants should hold a principal employment outside the institution. In so doing, they have made it clear that they did not want to face such a situation again.

Promotion

Each category, in line with the general pattern of the French civil service, is divided into several groups -each group (corps) being defined by a common set of rules concerning recruitment, promotion, pay, working conditions, and duties. Inside a group, there are subgroups or classes (classe), each with a prescribed salary scale. The scale is made up of "stages", to each of which a level of basic income is attached. Promotion from one stage to the next inside a given class is based solely on seniority, according to a predetermined schedule. Inside a corps, promotion from one class to the next is decided competitively within the limits of the number of vacant positions, partly at the level of the institution, partly at the national level. Transition from one corps to the other is not considered promotion but rather recruitment, all applicants formally submitting to the same procedures as in the concours.

Recruitment to positions such as head of department, dean, director, or even president, although they attract supplementary compensation, are not considered promotions because they are temporary. Terms of office for these positions range from three to five years, and presidents cannot be immediately reelected. Extra compensation, in the shape of bonuses or special allowances, ends with the term of office. Inside a statutory group, promotion is granted to an individual by means of two different procedures. Every year, a number of promotion opportunities (depending on budgetary appropriations, rate of turnover, etc.) are available for the ministry to allocate to the universities and to the CNU. Academic staff members who meet given criteria may apply to their institution for promotion. If they are not promoted, their applications are forwarded to the CNU, where they get a second chance. This dual procedure is justified by the fact that institutions tend to promote those who are involved locally in teaching, service, or administration, whereas the CNU tend to promote on the basis of "quality", usually focused on research quality as measured by quantitative indicators of written work. Inside a given class, promotion, in the sense of an increase in salary, is awarded automatically after given periods of time that are prescribed by the statutory regulations of the group. About every two or three years, tenured academics progress on a pay scale (i.e., "climb a step on the ladder").

Staff appraisal and staff development

Whereas secondary education teachers working in schools are evaluated by the headmaster and by the national education inspectorate, those teaching in higher education are evaluated by the heads of institutions. This evaluation is usually rather formal and has a limited impact on their careers. For staff of higher education status, there is no systematic evaluation of performance either in teaching or in research at the institutional level. Evaluation of teaching programmes in universities, in a process involving students, was first introduced by legislation in 1984, abolished in 1987, and reintroduced in 1997. Universities are allowed to conduct surveys based on student opinion, provided the assessment of individual performance of the teacher is accessible only to the person concerned. Only collective assessments of programmes can be brought to the attention of the senate, the various boards and, the administration of the institution. Application for a promotion is the only event that can trigger an evaluation of individuals by their peers of the university council, at the institutional level, or the CNU, at the national level. Despite renewed invitations to these bodies to take into account all aspects of an individual's activity, research perform-ance, measured mainly by number of publications, is still the dominant criterion.

In the absence of an application for promotion, no assessment is made of an individual's performance in research. Formal evaluation of research activity is conducted only at the collective level of the research institutes and departments. Staff development is not addressed, except on a voluntary individual basis. There are training programmes on offer for which staff may apply, along with funding. There is also the possibility for staff to ask for a sabbatical leave (up to one semester every six years) to allow for a shift in research programme or to get acquainted with a new field or a new technique. These opportunities are not directly related to any type of formal evaluation.

Employment and working conditions

Tenured staff of higher education status (teacher-researchers) are supposed to spend half their working time teaching and half on research. This explains why their teaching load is half that of staff of secondary education status, who are not expected to engage in research. By statute the teaching duties of staff are defined as time spent in the classroom with students. According to the type of interaction with students and the number of students, classes carry a different weight in the calculation of total teaching time. Such a quantitative definition of teaching duties creates a number of problems for institutions.

Although there are provisions for a reduction or a suspension of teaching duties for academics who are elected or appointed to administrative positions in universities or schools, nothing is prescribed concerning other forms of involvement in teaching -such as, for example, the design or delivery of new programmes in continuing education or distance education or the use of new technologies. Institutions are tempted to disregard this regulation of teaching, but the threat of litigation and controls by the inspectorate interferes with the development of innovative programmes and activities.

Conversely, the fact that staff duties are defined solely in relation to teaching makes it difficult for institutions to regulate the total involvement of teaching staff. Provided they are physically present in the classroom for the prescribed number of hours, academics sometimes consider their obligations to the institution fulfilled, leaving them free to organise the rest of their time. As a result, it is not unusual to hear complaints from students that their only opportunity to interact with their teachers is to approach them at the end of lectures. Apart from laboratory-based disciplines in which academics spend most of their time in their offices or in their laboratories, a continuous presence on the university premises is not expected of the teaching staff. As a result, a significant number of professors or lecturers live far away from the institutions that employ them and spend only one or two days on site. This lack of accountability is obviously linked to the high potential mobility allowed by the national status of staff, which allows them to transfer from one institution to another. This is also related to the practical and legal limits to evaluation of the various tasks they are supposed to perform.

The teaching load of professors, lecturers, and contract staff at the same level (associate professors, and lecturers, and temporary assistants) is set annually at 192 hours. According to the actual length of terms, which varies from 24 to 32 weeks per year, this amounts to an average weekly load of 6 to 8 hours. As there is some weighting of teaching time according to the instructional setting (seminar, lecture, practical work), the weekly teaching load of a professor can be as low as 4 hours. Hours taught in excess of this amount are paid as overtime at a flat rate that does not depend on the basic salary.

The teaching duties of staff of secondary education status amount to 384 hours per year, a weekly load of 12 to 16 hours, exactly twice that of other tenured staff. The rationale for this difference is that, although many of them are involved in some kind of research activity, it is not a part of their formal obligations. Student assessment and basic participation in the institution's administration are considered a part of the duties of academic staff. This implies that there is no extra compensation for these tasks: organising and supervising departmental exams, grading papers, holding vivas, sitting on university examination boards (e.g., for the baccalaureate, which is formally the first grade of higher education, although it actually marks the end of secondary education). However, heavier administrative responsibilities may be rewarded by bonuses. Time budgets have only recently drawn the attention of the decision makers in French higher education. The typical attitude used to be that the statute says that professors teach half the time and engage in research half the time, there was no reason to deviate from that rule. In fact, the involvement of staff in different activities is highly variable, as surveys conducted in the early 1990s in several universities have shown. In most disciplines, professors spend more time on administration than do other academic staff. Nontenured junior staff (attachés, moniteurs) spend more time on research than on teaching; they also spend more time on research than do most other categories of staff.

Pay scale and current earnings

Basic compensation

Basic compensation is based on gross salary, from which some social contributions are deducted and to which general bonuses and allowances are added. Gross salary is calculated by multiplying a monetary amount by an index, specific to each individual's position and seniority. This amount, the value of an index point, is the same for all public employees and can be altered by the government to adjust the total salary expenditure for the public sector.

Bonuses and allowances that are paid to all staff in a given category are usually included in basic compensation. These include the "residence allowance" (indemnité de résidence), from 0 to 3 percent of basic salary and the quite modest higher education bonus (prime d'enseignement supérieur or prime de recherche et d'enseignement supérieur), accounting for 3 percent to 6 percent of basic salary (see Figure 1).

Supplementary compensation

A survey conducted in 1989 (Ponthieux and Berthelot 1992) throws some light on the actual discrepancies in academic income among staff whose basic compensation is similar. Academic income is made up of all payments to staff as a result of their activity at the institution. The difference is explained by overtime teaching, other supplementary compensation (such as exam fees or night duties in hospitals) and specific bonuses or allowances attached to responsibilities and functions temporarily held by some staff members. The survey revealed pay differences across the disciplines. Faculty at medical schools stand out in the comparisons because many are entitled to a double salary, as teachers and as members of the university hospital staff. Overtime teaching is quite common: in 1989, 49 percent of academic staff outside the health sector worked overtime with an average of 30 hours a year, leading to a yearly supplementary compensation of about 5 percent. In a few exceptional cases, overtime may raise the salary of individual faculty by up to 50 percent (see Table 4).

In 1988, a comparative study of pay in the public sector highlighted the relative disadvantage of academic staff compared with other qualified public-sector employees. If their basic salary was established at a level comparable with army officers or top administrative civil servants, they were far behind in terms of bonuses and perks. In some departments, such as the Ministry of Finance, such supplementary income can double the take-home pay of administrators. In 1989 a series of bonuses were introduced in higher education aimed at making up in part for pay discrepancies and, hopefully, to introduce some kind of financial incentive for academic staff. Three new bonuses were to be awarded on a contractual basis to individuals engaging for a given period in "supplementary" tasksthat is, tasks not explicitly prescribed by the statutory regulations. "Outside" income Although academics are supposed to secure from their employer permission to undertake outside activity and to declare their outside income, there is very little information available on this matter except in the 1989 survey. It indicates that outside income of academic staff is very unevenly distributed, across positions, disciplines, and regions. Broadly speaking, Paris and the southern part of France account for half the number of academic staff and more than 70 percent of total outside income, when the other half get less than 30 percent. In most disciplines, except law and medicine, lecturers and professors have comparable outside income. Law, economics, and business studies are the sectors in which outside income is higher and most concentrated among a small number of people, while medicine is the sector in which it is more evenly distributed. The largest part of outside income comes from publication royalties but professional income is not negligible, especially in medicine and in law, where professors are entitled to a private practice.

In 1999, the Parliament voted into law an act on innovation in public higher education and research institutions that reveals a change of attitude toward outside income of academics involved in collaboration with industry, in research and development, or consultancy. Subject to authorisation by their universities, all academics will be allowed to work as consultants or scientific advisers with firms or agencies, within stated financial and time limits.

Social security and social benefits

Basic health insurance is similar to that of other workers but it is managed by a separate fund called Mutuelle de l'éducation (MGEN), which also offers optional supplementary insurance. Family benefits are, contrary to the situation of private-sector employees, paid by the Ministry of Education itself. Staff get the same basic allowances and benefits as other employees, but there are some supplementary allowances specific to public employees. Retirement pensions are also funded directly by the state out of its regular budget. Pensions are, for the time being, better in the public sector than in the private sector. The basic pension after 37 years and six months of employment is equivalent to 75 percent of the basic salary before retirement.

There are tax deductible supplementary pension funds for public employees, but their use is not widespread.

Comparability of academic staff salary

The income level of academic staff is easily comparable with that of the public civil service of which it is a part. Professors are paid at the same level as army generals or high court judges, as far as basic salary is concerned. What creates differences among public employees is the bonus system, which is both highly variable and almost secret. As specific bonuses may reach 10 percent of basic income in higher education, they frequently amount to 50 percent in some administrations, such as the Finance Ministry. Disclosure and adjustment of these bonuses between the different branches of the state administration has been on the agenda for decades without much progress being recorded.

Comparability with income levels in the private sector is difficult to assess. The tradition of secrecy is very strong in the French business community. One way to estimate discrepancies in pay is to look at transfers between the higher education sector and private industry. They exist only in a small number of sectors where universities have a hard time keeping highly qualified staff. The computer industry and the medical sector are known to offer academics opportunities for private employment. In these specific fields, possible solutions to avoid the brain drain are more generous compensation of staff, as in the medical schools, or more open opportunities to secure outside income, as in computer science and the high-tech sector. On the whole, after allowing for such specific adjustments, the general feeling of academic staff is that their pay level is adequate. However, no systematic survey has been conducted on this matter.

Current issues

Working conditions of academic staff have been affected by the growing number of students and by constraints on resources. One problem area was the availability and quality of teaching, laboratory, and office space. Until the end of the 1980s, the situation deteriorated with the rapid increase in student enrolment. In the early 1990s, a vast building programme (University 2000), jointly financed by the state and the regions, was launched. The situation improved greatly in many institutions, with the exception of institutions in Paris that are expected to catch up with the rest of France in the next few years.

Working conditions are also affected by the number and qualification of support staff. The ratio of nonteaching to teaching staff has steadily deteri-orated over the last decades. If the number of academic staff has grown over the period at the same pace as that of students, the number of administrative and technical staff is lagging. Academics complain of spending more and more of their time at tasks that could be performed by qualified clerical or scientific staff. The qualification structure of administrative staff, in particular, is inappropriate: the increased autonomy of institutions and departments has created the need for more qualified administrators that has not been met by new positions at the appropriate level. Now that the period of rapid growth has come to an end, there are worries about career perspectives of research students and junior academic staff. Forecasts of the demand for Ph.D. graduates are difficult to make, owing to possible shifts in students' choice of subjects and uncertain future policies in education and research. The number of tenured positions opened each year depends on government higher education policy and on the turnover in existing positions. Policy shifts have been frequent in the last decades. Eventually, the current slowdown of student enrolments will translate into a decrease in the overall number of new positions. This decrease will be offset for a while (until about 2005) by a higher turnover, as the large number of academics recruited in the 1970s will reach 65, the age of compulsory retirement.

At the level of the disciplines, the demand for new academic staff is governed by the age structure of the faculty and by shifts in student enrolments. As a consequence of the prevailing principles of open entry and free choice of subject for students, big shifts in enrolments are possible, as happened, for example, in recent years in psychology or in sports. This is likely to induce swings in the recruitment needs of academic staff as disciplines get more or less popular with students.

In some disciplines, prospective demand for Ph.D. holders should also include public and private research job openings. Private demand is highly volatile and depends on the state of the economy.

Recruitment for public research jobs (which has been an important factor in the 1970s) has declined in recent years, and is likely to remain so. The number of researchers in the CNRS, after a period of growth in the late 1980s, is now stable. The administration of CNRS has tried to induce researchers into applying for university positions, thus increasing competition for new Ph.D.s. The increase in the number of teaching-only staff in higher education is also a source of concern. In the last 15 years, the number of secondary school teachers seconded to higher education institutions has increased fourfold. Teaching-only staff now represents nearly 20 percent of tenured academic staff. This was partly the outcome of a short-term education policy: excess staff from secondary education, where enrolments were falling, were trans-ferred to higher education, which was still growing. Nevertheless, there are fears that this also indicates a deeper evolution of higher education, away from the tradition of teaching based on research.

Conclusion

After the basic regulatory changes of the mid-1980s, the statutory framework of academic staff seems stable at present. Neither recruitment procedures nor the system of tenure were being discussed in the late 1990s. French faculty enjoy a high level of individual and collective freedom. The independence of professors has been recognised as a constitutional principle by the constitutional court. Any regulation introducing elements of administrative control over their career or setting limits to their academic freedom can be brought before administrative courts that have the power to reverse decisions made on recruitment or promotion by administrative or academic authorities. In matters such as setting standards, designing curriculum, organising teaching, and expressing ideas in the classroom and in publications academics, individually or collectively, enjoy great autonomy. The only constraints that may be brought to bear on individual faculty results from collegial decisions made by peers. The priority of present higher education policy of the government is to strengthen the autonomy of the institutions. Universities are expected to develop their own policy. They are asked to innovate and improve the quality of research as well as of teaching. This might involve a transformation of the present incentive structure (possibly limiting the amount of overtime teaching by permanent staff).

University heads have long insisted on teaching performance being taken into account, alongside with research, in the recruitment and promotion processes. The main change in the financial conditions of academic staff has been the introduction of bonuses in order to create incen-tives and recognise the various tasks performed by academic staff. This was paralleled by attempts to introduce evaluation of the various activities at the institutional level. Despite the fact that such evaluation is still very limited and not linked to some sort of performance-based salary, it is perceived as the most threatening by academic staff; they were accustomed to broad individual autonomy and to distant checks and controls.

Because of the statute and traditions, however, universities find it very difficult to mobilise their human resources and to design better incentives for their staff. The national framework of employment and the weight of the discipline-based bodies that decide on the individual careers of academics explain the very weak ties that exist between them and the institutions in which they work. The great individual autonomy of the faculty conflicts with the autonomy of the institution and prevents urgently needed changes. The question is how to implement an institutional policy of quality improvement when the activity of the essential actors cannot be assessed and encouraged at the local level. How can departments be restructured in times of decreasing enrolments when disciplinary allegiance turns colleagues into enemies. While other countries contemplate abandoning the civil service status of academics, the question is not seriously debated in France although it is part of the platforms of some right-wing political parties. Every step that might be taken to increase the decision-making power of the universities on issues concerning tenured staff is feared by academics and fought by their unions on the basis of academic freedom and the mission of public service assigned to universities. The only incentives available are the various bonuses that universities can pay to their staff. These are fairly small in amount and, owing to the egalitarian culture of the French public service, spread over a large number of people, which limits their effect. However, despite many changes in their condition, it seems that the majority of French academic staff are quite satisfied with their present situation, contrary to periods in the recent past where there were frequent demonstrations and open conflicts. This contrasts with a growing discontent among the heads of institutions and many analysts of higher education, who see in the rigidity of the present legal framework an obstacle to adjustments to the more momentous changes that the whole sector is about to experience. The present quietness might turn out to be the calm that precedes the storm.

Reference

Ponthieux, S. and Berthelot, J.-M. (1992). Les enseignants-chercheurs de l'enseignement supérieur: Revenus professionnels et conditions d'activité. Documents du CERC, n°105.

  A research supervision bonus (prime d'encadrement doctoral et de recherché) provides about FF 20,000 for lecturers and up to 40,000 per year for professors. It is the most attractive bonus, being the largest and the most prestigious. It may increase the basic pay by 10 percent. A teaching bonus (prime pédagogique), initially linked to the amount of overtime teaching, was recently reoriented to reward effective work in the designing and management of teaching programmes. The amounts awarded are smaller and set uniformly at the national level but vary with the academic rank. An administration bonus (prime administrative, prime de charges administratives) is awarded to academics holding top administrative positions, partly allocated by the ministry, partly by universities. Its value is set proportionally to the weight attached by the ministry or the institution to the administrative functions. For university presidents, it amounts to 15 to 20 percent of basic salary.

  The proportion in the top class is limited to 10 percent of the whole category at the national level. The overall professor/lecturer ratio is quite stable. In 1989, the ministry agreed with unions on target ratios for the different categories of staff: 60/40 for lecturers/professors, 50/40/10 for second class/first class/special class professors, and 28/72/8 for second class/first class/special class lecturers. The present ratios are 68/32, 53/37/10, and 42/51/7, respectively. Nevertheless, such ratios vary quite a bit among disciplines and among institutions.

There are three classes in the statutory group of professors (second, first, and exceptionnelle) and three in the statutory group of lecturers (second, first, and hors-classe).

French academics: Between the professions and the civil service


Alonso Patron-Perez
email: alonso@robots.ox.ac.uk

Eric Sommerlade

Ian Reid

Action recognition using shared motion parts

In this paper we analyse the advantages of a joint boosting method, previously known in the object recognition community, to detect and classify action keyframes. The method focuses on sharing object parts among action classes. Instead of sharing parts that only encode shape similarities, we propose to include motion information as an extra clue for sharing. We show that the inclusion of motion information significantly improves the recognition accuracy. The method is tested using a standard action database containing 10 action classes obtaining perfect classification. It also yields promising results on complicated videos including complex background.

Introduction

The detection and classification of human actions is an active field of research. It has a wide range of applications from intelligent surveillance and game applications to video retrieval and content compression. As a result of this, a great variety of methods has been developed following different approaches. These methods try to overcome some of the main difficulties inherent in this task, namely, occlusions, scale changes, moving background, view-invariance, simultaneous actions and camera motion, examples of which can be seen in Fig. 1. Many of these problems have already been addressed in the object recognition community, and we take advantage of this knowledge for the action recognition problem.

One approach is to represent each action by a subset of still frames that best describes it. These frames are called keyframes and represent specific poses of the human body when performing an action. This is a simple but powerful model especially when combined with other cues as we show in this paper. Many of the methods based on keyframes try to match complete human silhouettes which requires accurate segmentation of subjects from the background. Another approach in action classification methods is to use parts. In this context by a part we mean an image or video fragment. Part-based models have several inherent advantages over other types of methods: they are fairly robust to occlusions, simple to extract and flexible in their representation. They also give a well-tested framework for detection and classification and for these reasons we choose to work with them. However, as noted by Torralba et al. [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF], a disadvantage of this model is that the number of parts needed to represent different classes grows linearly with each new class that is added.

The insight presented in [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF] was that many objects share similar parts and that when a new class is added we can reuse previously learned parts to classify the new one, therefore keeping the number of new parts that have to be added to a minimum. We apply this idea to human actions by noticing that different actions not only have similarly shaped parts but also many of the parts move in a common direction. We recognize that although the number of action classes is not a current problem due to the small size of the available human action databases (which will probably increase in the near future), this method has other advantages like reducing the time needed for detection and recognition, a by-product of having a smaller set of parts to represent an action set.

The contribution of this paper is to show that we can combine keyframe and part-based models by sharing parts among classes. This new combination of existing disparate methods enables us to describe different action classes using a compact representation and achieve high detection and classification accuracy.

The remainder of the paper is organised as follows: section 2 reviews related work on keyframe and part-based models. Section 3 is divided in two parts: the first one presents the specific feature extraction method used in this paper while the second one describes the basics of the joint boosting method designed for the explicit sharing of features among classes. In section 4 we show the results of the classification for a small database and some qualitative experiments in more complex scenes which demonstrate how this method could be extended to deal with such difficult cases.

Related work

The use of spatio-temporal parts (video fragments) has become popular in recent years, and several methods have been presented where only the response function used to detect interest locations in the video is changed. Laptev and Lindeberg [START_REF] Laptev | Space-time Interest Points[END_REF] extracted video fragments around interest points located in regions of motion discontinuity. Dollar et al. [START_REF] Dollár | Behavior recognition via sparse spatio-temporal features[END_REF] used locations where periodic motion was present and Oikonomopoulus et al. [START_REF] Oikonomopoulos | Spatio-Temporal Salient Points for Visual Recognition of Human Actions[END_REF] used regions of local maximum entropy. Niebles et al. [START_REF] Niebles | Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words[END_REF] used the same response function as Dollar but changed the classification method using a pLSA (probabilistic Latent Semantic Analysis) approach. More recently Niebles et al. [START_REF] Niebles | A Hierachical Model of Shape and Appearance for Human Action Classification[END_REF] presented a method that combines static features and spatio-temporal features using a hierarchical constellation model. Laptev has gone one step further trying to recognise actions in movies [START_REF] Laptev | Retrieving Actions in Movies[END_REF][START_REF] Laptev | Learning realistic human actions from movies[END_REF] using keyframes to obtain possible location hypotheses and validating these with temporal information. He uses AdaBoost for selecting distinctive video fragments that can be used for classification.

This begs the question: how many frames are needed to describe an action?. This question has been treated recently in [START_REF] Schindler | Action Snippets: How many frames does human action recognition require?[END_REF][START_REF] Wang | Unsupervised Discovery of Action Classes[END_REF], where the results show that even with few frames a high classification accuracy can be achieved. Their method does not provide detection; just classification of actions given an already segmented sequence of frames centred on the subject. Action recognition with keyframes has also been tested by Carlsson and Sullivan [START_REF] Carlsson | Action Recognition by Shape Matching to Key Frames[END_REF][START_REF] Sullivan | Recognizing and Tracking Human Action[END_REF] relying completely on a shape matching algorithm and by Efros [START_REF] Efros | Recognizing Action at a Distance[END_REF] using a pure motion descriptor. It has been shown [START_REF] Niebles | A Hierachical Model of Shape and Appearance for Human Action Classification[END_REF][START_REF] Schindler | Action Snippets: How many frames does human action recognition require?[END_REF][START_REF] Ke | Event Detection in Crowded Videos[END_REF][START_REF] Davis | Recognizing Movement using Motion Histograms[END_REF][START_REF] Dalal | Human Detection Using Oriented Histograms of Flow and Appearance[END_REF] that a combination of shape and motion increases the detection and classification performance. Our approach follows the spirit of these papers; we will show that many actions can be described by a single keyframe.

The problem with keyframes is that when the number of actions increases, it is harder to find a single frame that can distinguish a particular action from the rest. In this case, motion can be used to disambiguate between them.

Shared part model

The main observation made by [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF] was that there are parts that can be shared among different object classes, thus greatly reducing the final number of parts needed to learn a classifier. We show that this can be directly applied to human action recognition by adding motion information to each part; for example, waving with one or two hands, and jumping jack all share similar arm motion.

Feature vector estimation

Here we describe how we generate a pool of feature vectors from which the boosting method will select an optimal subset during training. First, from a set of action keyframes we select a fixed size region of interest centred around the person performing the action. If the people in the training examples present a significant difference in scale, they have to be normalized to a fixed size. We also extract two optic flow fields between the keyframe and its previous and subsequent frames using the Lucas-Kanade method [START_REF] Lucas | An Iterative Image Registration Technique with an application to Stereo Vision[END_REF] (Fig. 2). We use the orientation of these flow fields by discretising it to one of n directions if its magnitude is above a given threshold. Then we extract M image fragments randomly from these regions (varying in size from 7x7 to 15x15). For each fragment the following information is also saved: a spatial binary mask (that represents its position with respect to the object centre) and the dominant orientations of the two related optic flow fields that lie directly in the image region that the fragment covers. Dominant orientations are estimated by histogramming the optic flow orientations of every pixel inside the fragment and choosing the bin with the highest frequency.

The feature vector x from a training image is calculated as follows: the jth element of the feature vector is obtained, first, by applying normalized cross correlation (NCC) between the training image and the jth extracted fragment and exponentiating the response to a power p. By using higher values of p we are effectively performing template matching (we will use p = 10 in all the experiments as suggested in [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF]). Unlike [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF] we weight this template matching value by a factor w of , which measures the optic flow similarity between the fragment and all regions of the training image. The weight at a specific image location ℓ = (x, y) is a normalized value in [0, 1] (1 meaning no difference and 0 a maximum distance of 180 • ) calculated as the difference between the angles of the previous and subsequent dominant optic flow orientations in a region centred at ℓ (and of the same size as the jth fragment) and the respective dominant flow orientations of the fragment. Finally, a convolution with the fragment's spatial mask is performed which can be seen as the fragment voting for a probable object centre. The size of the binary spatial masks allows small translations of the original fragment location. The response of this function at the object's centre is taken to be the jth element of the feature vector; this is expressed in equation 1. We obtain a feature vector for each training image by applying this process with each extracted fragment. The process of estimating the jth element of the feature vector for a training image is illustrated in Fig. 3a displaying the response value of all fragments is shown in Fig. 3b).

In the testing phase, we follow the same procedure, constructing feature vectors at each image location and classifying these as containing one of the action classes or not. For a location ℓ of an image I at scale σ the feature vector is given by:

x j (ℓ, σ) = m j (ℓ) * (|I σ ⊗ g j | p • w of ) (1)
where [⊗ * •] represent the NCC, convolution and elementwise multiplication operators respectively, g j is the jth fragment and m j (ℓ) its spatial binary mask centred at location ℓ. As can be seen from the definition of equation 1, the method becomes multiscale by constructing a Gaussian pyramid of the input image and searching in a range of scales. To avoid optic flow estimation errors in smaller scales we only calculate optic flow at the original scale. When estimating the optic flow weight value for a specific region in a smaller image, we look for the weight of the corresponding region in the original scale.

Joint boosting method

The idea of the joint classifier presented in [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF] is based on a modification of the popular AdaBoost (Adaptive Boosting) known as GentleBoost [START_REF] Friedman | Additive logistic regression: a statistical view of boosting[END_REF]. As in any boosting method, we try to construct a strong classifier F (x, c) by adding up several weak classifiers f m (x, c) (equation 2), where x is the input data (in our case the feature vector described in the previous section) and c is the class.

F (x, c) = M m f m (x, c) (2)
Before getting into more details of the Joint boosting method, we review the learning process of a standard AdaBoost algorithm. At each boosting cycle, a weak classifier is chosen that best classifies the weighted training examples. This weighting is obtained by giving a higher weight to the examples that were misclassified in the previous round, therefore biasing the selection of the next weak classifier to perform better on these erroneous classifications. Given a test example, each weak classifier makes a binary decision whether it belongs to a specific class or not. Normally we obtain a final classification by taking the sign of the sum of the weak classifiers' response.

As we have seen boosting algorithms are binary classifiers, and, when dealing with multiple classes, strong classifiers have to be trained independently for each class. The result of this process is that the selected weak classifiers are very class-specific.

In contrast, the joint classifier trains all strong classifiers simultaneously and is designed to favour more general weak classifiers that can be shared among different classes. This translates to the inclusion of a new criterion for the selection of a weak classifier: we have to check not only the reduction of misclassification error for a single class, but the overall error reduction if we use that same classifier to separate a subset of classes from the rest (including the background). The function that we want to minimise is given in equation 3, where z c i are the data labels {-1, 1} of the training set, which is an extended version of the regular exponential error function used in many boosting methods (the only difference being the summation with respect to the classes).

J = C c=1 E[e -z c i F (x,c) ] (3)
The above cost function is minimised similarly to the regular GentleBoost algorithm using adaptive Newton steps [START_REF] Friedman | Additive logistic regression: a statistical view of boosting[END_REF]. In other words, at each round of the boosting algorithm we choose a feature and the best subset of classes S that minimises the weighted square error:

J wse = C c=1 N i=1 w c i (z c i -f m (x i , c)) 2 . ( 4
)
where w c i = e -z c i F (xi,c) are the weights estimated for example i using the strong classifier for class c. For weak classifiers, we use stumps (decision trees with one node), that consist of a simple thresholding of a random input variable, which for a selected subset of classes S take the form:

f m (x, c) =    a S if x j > θ and c ∈ S b S if x j ≤ θ and c ∈ S k c S if c / ∈ S (5)
where x j represents the jth entry of the input feature vector (see the previous section for an explanation of how this vector is calculated). Another way of understanding the above equation is to consider a weak classifier as a function f m (x) = aδ(x j > θ) + bδ(x j ≤ θ), where a and b are regression parameters. Each weak classifier needs to save the subset of classes S, the parameters (a S ,b S ,θ,j) and a set of class-specific constants k c S similar to [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF] to avoid selecting a class due to an imbalance among the number of positive and negative examples in the training set. This is done by testing all possible combinations of classes, features and thresholds and choosing the ones that minimise equation 4. Given a subset S a feature j and a threshold θ the values of a S ,b S and k c S that minimise equation 4 are given by:

a S (j, θ) = c∈S i w c i z c i δ(x j i > θ) c∈S i w c i δ(x j i > θ) , (6)
b S (j, θ)

= c∈S i w c i z c i δ(x j i ≤ θ) c∈S i w c i δ(x j i ≤ θ) , (7)
k c = i w c i z c i i w c i c / ∈ S. (8)
Testing all possible subsets of classes is exponential in the number of classes. This makes a naive training implementation very slow. To speed up the process, we implement some of the modifications suggested in [START_REF] Torralba | Sharing visual features for multiclass and multiview object detection[END_REF] but keep a complete search over the sharing combinations.

Experiments

For the experiments we used the Weizmann database compiled originally for [START_REF] Blank | Actions as Space-Time Shapes[END_REF]. This database presents 10 actions (bending, jumping jack, jumping, jumping in place, running, galloping sideways, skipping, walking, one-hand waving and two-hand waving) performed by nine individuals. It is a simple dataset containing only one person per video and no complex background.

For training we manually extract one keyframe and its surrounding frames from each video to estimate optic flow (Fig. 2). We then extract 2250 image fragments from this set. We adopt a leave-one-out cross validation scheme and an eight bin discretisation of the optic flow for all testings.

Prior to performing experiments with complete videos, we tested the difference between detection and classification performance using only shape information and shape plus motion. For these simpler experiments we tested frames selected to contain a keyframe. Training both classifiers using 100 stumps, the shape-only classifier had a poor overall classification accuracy of 37.5%, compared to the 92.9% of the shape-motion one. Adding 500 background negative examples (taken from random images) to the shape-only classifier helped to raise its performance up to 64.9% still far below the shape-motion classifier. Fig. 6 shows some of the results obtained with the shape-motion classifier.

To classify the entire video database, the following procedure is performed. We use this classifier to test the video database and collect false positives. Second, we retrain the classifier adding the false positives as negative examples. The final multiclass classifier is trained using 200 stumps (Fig. 4). For the testing part, we search the first 60 frames of each video for keyframes. Local maxima detections in a temporal window of 10 frames are selected. These selections vote for their class according to their classification magnitude. A frameby-frame example of the classification magnitude of some tested videos is shown in Fig. 7. It can be seen that local maxima correspond to correct keyframe detections. Following this testing framework we achieved 100% percent of detection and classification accuracy. Comparing classification and localisation results with other works in the literature is difficult due to the very method-specific testing procedures developed; yet, to put our results in context we present a summary of the best classification results obtained using the Weizmann database in Table 1. This results consist of correct classification of whole video sequences. It is also worth to notice that the methods in [START_REF] Schindler | Action Snippets: How many frames does human action recognition require?[END_REF] and [START_REF] Wang | Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model[END_REF] don't localise actions.

Method

Accuracy Shared shape-motion parts 100 % SCHINDLER [START_REF] Schindler | Action Snippets: How many frames does human action recognition require?[END_REF] 100 % BLANK [START_REF] Blank | Actions as Space-Time Shapes[END_REF] 100 % JHUANG [START_REF] Jhuang | A Biologically Inspired System for Action Recognition[END_REF] 98.8 % WANG [START_REF] Wang | Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model[END_REF] 97.8 % NIEBLES [START_REF] Niebles | A Hierachical Model of Shape and Appearance for Human Action Classification[END_REF] 72.8%

Table 1. Comparison of the classification accuracy for the Weizmann database of our method with the results obtained in the latest papers.

In order to get insight into the method's performance in complex scenes, we tested a classifier trained with the Weizmann database, with the videos used in [START_REF] Ke | Event Detection in Crowded Videos[END_REF]. In these videos five of the 10 learned actions are present (bending, jumping jack, walking, one-hand waving and two-hand waving). Examples of these videos can be seen in

Conclusions

In this paper we showed that a simple keyframe technique is enough to detect action in videos. We added motion information to a well-known boosting method, improving its accuracy for the task of human action detection and classification. We also showed promising results in complex scenes that could be improved by adding a proper set of training examples.

Because of the good results showed by other methods using the shame action database further comparisons are required to highlight the advantages of our method, namely, recognition time, number of parts used and performance as a function of the number of training examples. All these will be addressed as part of our future work.

Despite the obtained results, we noticed some shortcomings of the approach. Currently, we are using intensitybased fragments and NCC as a shape similarity measure; this has the disadvantage of lacking robustness when matching people wearing different clothes, relying on the amount of training examples to overcome this difficulty. Future work on this topic will include better shape and motion descriptors for the fragments, in particular histograms of oriented gradients (HoG) that have shown a good performance in [START_REF] Laptev | Retrieving Actions in Movies[END_REF].

Histograms of Optic Flow seem also like an obvious extension for adding robustness to the patch motion descriptor. Mantaining a more detailed record of all the flow directions inside the patch could help in selecting which directions are important for an action patch and wich are part of the background without having to rely in silhouette segmentations.

Other improvements include automatic keyframe selection and quantitative testings in long video sequences including complex and cluttered backgrounds.

Figure 1 .

 1 Figure 1. Complex scenes illustrating some of the difficulties of the action detection and classification task.

Figure 2 .

 2 Figure 2. First row shows two different action sequences (running and galloping sideways) with their respective keyframe in the middle. The middle row shows the previous and posterior optic flow calculated using the Lucas-Kanade method. The bottom row shows the discretized optic flow in 8 directions (displayed in gray scale). Also shown are three examples of random patches selected and their respective dominant orientation in each of the actions. As can be seen the patches at the top part of the body could be shared by these two classes because of their simmilarity in shape and motion while the patch located near the legs could be used to discriminate between them.

Figure 3 .

 3 Figure 3. Generation of a feature vector. (a) An example of the process for one training image and one fragment. NCC and Optic Flow (OF) matching maps are computed. The OF is used to weight the NCC matching. Finally a convolution with the fragment's spatial mask is performed. (b) The complete feature vector for the training image in (a). Each element records the response value of one fragment.

Figure 4 .

 4 Figure 4. First 30 fragments selected by the boosting algorithm sorted by the number of classes (actions) that share them (marked with a white square). Optic flow orientations associated with the frame before and after the keyframe are shown in the second and third rows.

Figure 5 .

 5 Figure 5. The handpicked keyframes chosen to represent the actions: bending, jumping jack, jumping, jumping in place, running, galloping sideways, skipping, walking, onehand waving and two-hand waving.

Figure 6 .

 6 Figure 6. Testing results. Ground truth (red), correct classification and localisation (yellow), incorrect classification (blue).

  Training is divided in two phases: first a preliminary joint classifier is trained using only positive keyframe examples of each class (the negative examples of one class are the positive examples of the other classes).

Fig. 1 .

 1 Some results are shown in Fig. 8. These are divided in (1) correct detection and classification, (2) correct detection / wrong classification and (3) false positives. Many of the false positives were detected in videos with motion clutter. An explanation for this behaviour could be that the patches selected as part of the joint classifier during the training process didn't have enough discriminant power in motion cluttered scenes due to the lack of background motion in the training examples. Despite this disadvantage and the complexity of the videos, we were still able to detect several keyframes.

Figure 7 .

 7 Figure 7. Maximum value of the classifier in each frame in some of the testing videos (of the actions jumping jack, jumping in place and running), as we can see, high detection values correspond to matching keyframes (Fig. 5).

Figure 8 .

 8 Figure 8. Tests with a 200 stump classifier in scenes with complex background. First two rows, examples of correct detection and classification (yellow). Last two rows, examples of misclassification (magenta) and false positives (blue).

Acknowledgements

We would like to thank Yan Ke for allowing us the use of his action database. This work was supported by CONACYT and the EU FP6 project HERMES.