Zoltán Ernő Csajbók
email: csajbok.zoltan@foh.unideb.hu

Tamás Mihálydeák
email: mihalydeak.tamas@inf.unideb.hu

József Ködmön
email: kodmon.jozsef@foh.unideb.hu

An Adequate Representation of Medical Data Based on Partial Set Approximation

Keywords: Rough set theory, partial approximation of sets, tool-based approximation framework, thyroid disease diagnosis

Computer aided medical diagnosis and treatment require an adequate representation of uncertain or imperfect medical data. There are many approaches dealing with such type of data. Pawlak proposed a new method called rough set theory. In this paper, beyond classical and recent methods, the authors propose a basically new approach. It relies on a generalization of rough set theory, namely, the partial covering of the universe of objects. It adequately reflects the partial nature of real-life problems. This new approach called the partial approximation of sets is presented as well as its medical informatics application is demonstrated.

Introduction

Uncertainty, imprecision or incompleteness are important aspects of patients' medical data. Therefore, it is an inevitable challenge of medical informatics how imperfect medical data can be represented, moreover, how appropriate and powerful approximative inference methods can be provided. There are two classical approaches, Bayesian statistics [START_REF] Ashby | Bayesian statistics in medicine: a 25 year review[END_REF][START_REF] Spiegelhalter | Bayesian Approaches to Clinical Trials and Health-Care Evaluation (Statistics in Practice)[END_REF] and Dempster-Shafer theory of evidence [START_REF] Beynon | The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling[END_REF][START_REF]Advances in the Dempster-Shafer theory of evidence[END_REF]. In the middle of 1960s, Zadeh proposed fuzzy set theory [START_REF] Seising | Fuzzy sets in medicine -historical remarks[END_REF][START_REF] Seising | From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis[END_REF][START_REF] Zadeh | Fuzzy sets[END_REF]. The medical expert system MYCIN uses the so-called certainty factor (CF) model [START_REF] Buchanan | Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project[END_REF]. Recently knowledge-based systems and other artificial intelligence methods have also been used in the medical diagnosis and treatment [START_REF] Pandey | Knowledge and intelligent computing system in medicine[END_REF][START_REF] Patel | The coming of age of artificial intelligence in medicine[END_REF].

In the early 1980s, Pawlak proposed a new method called rough set theory [START_REF] Pawlak | Rough sets[END_REF][START_REF] Pawlak | Rough Sets: Theoretical Aspects of Reasoning about Data[END_REF]. It can be viewed as a new mathematical approach to manage imperfect or vague knowledge [START_REF] Orlowska | Logic of vague concepts[END_REF][START_REF] Keefe | Introduction: Theories of vagueness[END_REF]. It has extensive applications, among others, in artificial intelligence, cognitive sciences and medicine [START_REF] Hassanien | Rough sets in medical informatics applications[END_REF][START_REF] Straszecka | Combining uncertainty and imprecision in models of medical diagnosis[END_REF]. Its starting point is a nonempty finite set U of objects and an equivalence relation ε on U [START_REF] Pawlak | Rough Sets: Theoretical Aspects of Reasoning about Data[END_REF]. The equivalence classes are called ε-elementary sets. They can be viewed as sets of indiscernible objects characterized by the same available information about them [START_REF] Pawlak | Rough sets: Some extensions[END_REF][START_REF] Skowron | Vague concepts: A rough-set approach[END_REF]. Definable sets are any unions of ε-elementary sets.

Then, any set S ⊆ U can be naturally approximated by the so-called lower and upper ε-approximations of S. The former is the union of all ε-elementary sets which are the subsets of S, whereas the latter is the union of all ε-elementary sets which have a nonempty intersection with S.

In rough set theory, the equivalence classes are pairwise disjoint and cover the universe. Giving up the disjoint property but retaining the covering, a natural generalization of rough set theory is obtained. It is called covering-based rough set theory [START_REF] Bonikowski | Extensions and intensions in the rough set theory[END_REF][START_REF] Yao | On generalizing rough set theory[END_REF][START_REF] Zakowski | Approximations in the space (U, Π)[END_REF][START_REF] Zhu | Topological approaches to covering rough sets[END_REF][START_REF] Zhu | Relationship between generalized rough sets based on binary relation and covering[END_REF]. The partial nature of real-life problems, however, requires working out partial approximation schemes. Thus, not only the pairwise disjoint property but also the covering of the universe are given up. This basically new approach is referred to as partial approximation of sets [START_REF] Csajbók | A general set theoretic approximation framework[END_REF][START_REF] Csajbók | Approximation of sets based on partial covering[END_REF][START_REF] Csajbók | Partial approximative set theory: A generalization of the rough set theory[END_REF].

Furthermore, based on partial approximation of sets, a general tool-based approximation framework can be built up [START_REF] Csajbók | A general tool-based approximation framework based on partial approximation of sets[END_REF]. It is a general model which has applications in computer security [START_REF] Csajbók | A security model for personal information security management based on partial approximative set theory[END_REF][START_REF] Csajbók | Simultaneous anomaly and misuse intrusion detections based on partial approximative set theory[END_REF]. In this paper, we apply it to representing imperfect medical data.

The paper is organized as follows. Having reviewed the fundamental notions of partial approximation of sets in Section 2, Section 3 presents the general toolbased approximation framework. Section 4 shows a simplified thyroid disease diagnosis example in order to demonstrate how our approach can be applied. Finally, in Section 5, we conclude the paper.

Fundamental Notions of Partial Approximation of Sets

A general set-theoretic approximation framework has four components:

the domain of approximations whose sets are approximated; some distinguished sets of the domain as the basis of approximations; definable sets deriving from base sets in some way or other as possible approximations of sets of the domain; an approximation pair determining the lower and upper approximations of sets using definable sets.

Definable sets represent our available knowledge about the domain. They can be thought of as tools of the approximation process and simply called tools. In particular, base sets can be viewed as primary tools, definable sets as derived tools. The way of getting derived tools from primary tools shows how primary tools are used. An approximation pair prescribes the utilization of primary and derived tools in a whole approximation process.

Let U be a finite nonempty set. Let B ⊆ 2 U be a nonempty family of nonempty subsets of U . 3 B is called the base system, its members are the base sets. Let D B ⊆ 2 U denote an extension of B in such a way that 1) B ⊆ D B , and 2) ∅ ∈ D B . The members of D B are called definable sets. It is not necessary that D B = U . An approximation framework is total if D B = U , and it is partial otherwise.

It is a natural assumption that D B is obtained (derived) form B by some sort of set type transformations (for the most important cases, see [START_REF] Csajbók | A general set theoretic approximation framework[END_REF]). In order to build a generalized Pawlakian partial approximation framework, we define D B with the following definition:

1. ∅ ∈ D B ; 2. B ⊆ D B ; 3. if B ⊆ B, B , B ∈ D B .
Next, let l, u be an ordered pair of maps l, u : 2 U → 2 U on (2 U , ⊆). Of course, their intended meaning is to express the lower and upper approximations of any sets with the help of the beforehand given definable sets as tools. Hence, l, u is called an approximation pair.

Here, for any S ⊆ U , the lower and upper approximations are a possible generalization of Pawlakian lower and upper ε-approximations:

lower approximation l(S) is the union of all definable sets which are the subsets of S:

l(S) = {D ∈ D B | D ⊆ S} ∈ D B ;
upper approximation u(S) is the union of all definable sets which have a nonempty intersection with S:

u(S) = {D ∈ D B | D ∩ S = ∅} ∈ D B .
The universe U , the base system B, the set D B of definable sets, and the approximation pair l, u together are called the partial approximation space and denoted by the ordered 5-tuple 2 U , B, D B , l B , u B .

Possible interpretations of lower and upper approximations of a set S ⊆ U are the following [START_REF] Pawlak | Rough Sets: Theoretical Aspects of Reasoning about Data[END_REF]:

1. l(S) is the set of all members of U which can certainly be classified as belonging to S with respect to B (positive region); 2. u(S) is the set of all members of U which can possibly be classified as belonging to S with respect to B ; 3. U \ u(S) is the set of all members of U which can certainly be classified as not belonging to S with respect to B (negative region).

Elements of Tool-Based Approximation Framework

Tool-based approximation framework is defined relying on partial approximation spaces. For more details, see [START_REF] Csajbók | A general tool-based approximation framework based on partial approximation of sets[END_REF]. Let A + , A -⊆ U be two nonempty subsets of U in such a way that A + ∩ A -= ∅. A + and A -are called the positive and negative reference set, respectively. The adjectives "positive" and "negative" claim nothing else but that the sets A + and A -are well separated.

Furthermore, let T + , T -⊆ 2 U be two nonempty finite families of nonempty subsets of U , where T + and T -are not necessarily disjoint. The members of T + are called positive or T + -tools, whereas the members of T -are called negative or T --tools.

Requirements for positive and negative tools are the following:

sets in T + (T -) are not necessarily pairwise disjoint; neither T + nor T -covers U necessarily; for each subset T + ∈ T + (T -∈ T -), it is easy to decide whether a member of U belongs to T + (T -) or does not.

Mutual relationship between positive/negative tools and positive/negative reference sets may provide useful information. They can be obtained with the help of partial approximation of positive/negative reference sets within the approximation spaces 2

U , T + , D T + , l T + , u T + , 2 U , T -, D T -, l T -, u T -, respectively.

Thyroid Disease Diagnosis Support

Let us suppose that we study the symptoms of the thyroid dysfunction [START_REF] Ladenson | American thyroid association guidelines for detection of thyroid dysfunction[END_REF]. Clinical symptoms which manifest thyroid dysfunction progress slowly they are often nonspecific and could represent other not thyroid disorders. Hence, thyroid function diagnosis via clinical symptoms is an important but inexact classification problem [START_REF] Temurtas | A comparative study on thyroid disease diagnosis using neural networks[END_REF][START_REF] Ozyılmaz | Diagnosis of thyroid disease using artificial neural network methods[END_REF].

For the sake of simplicity, we deal with only hypothyroidism and hyperthyroidism thyroid disorders [START_REF] Ladenson | Thyroid[END_REF]. The thyroid gland produces thyroid hormones, thyroxine (T4) and triiodothyronine (T3). Hyperthyroidism occurs when the thyroid is "overactive", i.e., releases too much hormones, whereas hypothyroidism takes place when the thyroid is "underactive", i.e., does not produce enough hormones.

We have at our disposal an information table (Table 1) containing clinical symptoms which may indicate whether hypothyroidism or hyperthyroidism, or else neither of the two. There are, of course, more symptoms of hypothyroidism and hyperthyroidism, but we have simplified the example here for illustrative purposes.

Table 1. Clinical symptoms of thyroid dysfunction and diagnosis based on test results

No.

Weight change

Edema

Tachycardia

Increased sweating

Affection

Hypothyroidism

The universe of objects is the set of patients, i.e., U = {P 1 , P 2 , P 3 , P 4 , P 5 }.

Hypothyroidism and hyperthyroidism can be accurately diagnosed with laboratory tests. Last two columns in Table 1 are based on these results. Reference sets are formed on them: the "positive" reference set is: A Hypo = {P 2 , P 3 }, and the "negative" reference set is: A Hyper = {P 4 , P 5 }.

Note that A Hypo ∩ A Hyper = ∅.

Table 2 contains clinical symptoms which may indicate hypothyroidism ("positive tools") and hyperthyroidism ("negative tools"). Notice that T Hypo and T Hyper do not necessarily cover the universe ( T Hypo , T Hyper ⊆ U ) and their members are not pairwise disjoint, respectively. The evaluation of patients having hypothyroidism with respect to the clinical symptoms which may indicate hypothyroidism, i.e., in the language of partial approximation framework, forming the lower and upper approximations of A Hypo in the partial approximation space 2 U , T Hypo , D T Hypo , l T Hypo , u T Hypo (see Table 3 The evaluation of patients having hypothyroidism with respect to the clinical symptoms which may indicate hyperthyroidism, i.e., in the language of partial approximation framework, forming the lower and upper approximations of A Hypo in the partial approximation space 2 U , T Hyper , D T Hyper , l T Hyper , u T Hyper (see Table 4) (it can be viewed as a "cross-evaluation"):

l T Hyper (A Hypo ) = l T Hyper ({P 2 , P 3 }) = ∅.

Informally: in accordance with the expectations, there are no symptoms under which patients can certainly be classified as suffering from hypothyroidism with respect to the clinical symptoms which may indicate hyperthyroidism. -u T Hyper (A Hypo ) = u T Hyper ({P 2 , P 3 }) = T Hyper 2 = {P 3 , P 4 }. Informally: in accordance with the nonspecific nature of thyroid dysfunction symptoms, there are symptoms under which patients can possibly be classified as suffering from hypothyroidism with respect to the clinical symptoms which may indicate hyperthyroidism.

Conclusion and Future Work

In this paper, beyond classical and recent methods, the authors have proposed a basically new approach for handling imperfect medical data. Its novelty is inherent in the partiality of the applied approximation space. Partial approximation space is a generalization of Pawlakian rough set theory which adequately reflects partial nature of real-life problems. The proposed methods may serve as a basis of computer assistance systems in medicine.

The evaluation of evidence described above is of qualitative nature. Quantifying uncertainty is an important challenge in rough set theory and its generalizations. We will plan to make the model more useful in real applications, first of all, by means of quantifying uncertainty in partial approximation spaces.

1 ∪ (T Hypo 1 ∩ T Hypo 3 )= 1 ∪ T Hypo 3 ∪ (T Hypo 1 ∩ T Hypo 3 )

 1131313 ):l T Hypo (A Hypo ) = l T Hypo ({P 2 , P 3 }) = T Hypo {P 2 , P 3 }.Informally: patients having "weight gain" or "weight gain and tachycardia" can certainly be classified as suffering from hypothyroidism with respect to the clinical symptoms which may indicate hypothyroidism. -u T Hypo (A Hypo ) = u T Hypo ({P 2 , P 3 }) = T Hypo = {P 2 , P 3 , P 4 }.Informally: patients having "weight gain" or "tachycardia" or "weight gain and tachycardia" can possibly be classified as suffering from hypothyroidism with respect to the clinical symptoms which may indicate hypothyroidism.

Table 2 .

 2 Symptoms which may indicate hypothyroidism/hyperthyroidism ("positive"/ "negative" tools)

Table 4 .

 4 Approximating hypothyroidism with clinical symptoms which may indicate hyperthyroidism

No. Weight change Edem a Tachy- cardia Increased sweating

					Affection	Hypothy-	Hyperthy-
						roidism	roidism
	not change no	no	no	normal	no	no
	gain	no	no	no	normal	yes	no
	gain	no yes	no	normal	yes	no
	loss	no yes	yes	normal	no	yes
	not change yes no	yes	nervousness	no	yes

Acknowledgements

The publication was supported by the T ÁMOP-4.2.2.C-11/1/KONV-2012-0001 project. The project has been supported by the European Union, co-financed by the European Social Fund.

Primary tools are (see Table 2):

-T Hypo = {T Hypo

Sample evaluations can be carried out as follows.

Let us take, e.g., the group of patients having hypothyroidism diagnosis: A Hypo = {P 2 , P 3 } ⊆ U .


Omer Faruk Soylemez

Burhan Ergen
email: bergen@firat.edu.tr

EYE LOCATION AND EYE STATE DETECTION IN FACIAL IMAGES USING CIRCULAR HOUGH TRANSFORM

Keywords:

Recently, eye states are used as inputs to various applications such as facial expression recognition systems, human-computer interaction and driver fatigue detection systems. Especially with the prominence of human computer interaction, eye state detection has drawn great attention in the past decade. In this study, an eye state detection system based on Circular Hough Transform (CHT) has been offered. Initially, face and eye images are extracted from given gray-level images. After some preprocessing steps, existence of circular iris structure is searched within the extracted eye image using CHT. Existence of circular iris structure is searched within the eye image with the help of circular Hough transform. Eyes are decided as open if iris could identified as a circle.

Introduction

Detection of eye location and states has received a great deal of attention because eyes are the most important part of human face. Today, many applications are using eye states as inputs. Facial expression recognition, human-computer interaction and driver fatigue detection systems could be given as examples to such applications. In facial expression recognition systems, subjects mood could be estimated by utilizing eye openness along with other facial features [START_REF] Sandbach | Static and dynamic 3D facial expression recognition: A comprehensive survey[END_REF]. In driver fatigue detection systems, eye blinking count, time taken by a blink and frequency of eye blinks are combined together to form an alert and warn the driver in case of a driver fatigue level increase [START_REF] Wang | Driver fatigue detection: a survey[END_REF]. In human computer interaction, by tracing eye movement and focus, computers could acknowledge and process user requests [START_REF] Jacob | Eye tracking in human-computer interaction and usability research: Ready to deliver the promises[END_REF]. By employing these kinds of applications in academical and commercial fields, eye state detection has drawn great attention in the past decade. In the field of eye state recognition, studies are employing parameters such as iris, eye perimeter, eye edge and eye lids. Wang and Yang [START_REF] Wang | Eye location and eye state detection in facial images with unconstrained background[END_REF] used chain code tracing for extracting edges of iris and then used Circular Hough Transform (CHT) in order to detect iris circle. Tian, Kanade and Cohn [START_REF] Tian | Eye-state action unit detection by gabor wavelets[END_REF] took advantage of Gabor wavelets and neural networks in order to detect eye states. Seneratne et al. [START_REF] Seneratne | Driver Fatigue Detection by Fusing Multiple Cues[END_REF] compared eye blocks intensity values using support vector machines (SVM) and Naive Bayes (NB) classification methods to overcome eye state detection problem. Xu, Zeng and Wang [START_REF] Xu | Efficient eye states detection in realtime for drowsy driving monitoring system[END_REF] tried to detect eye states by using Adaboost based cascade classifier and a histogram property which is named as Local Binary Pattern (LBP) . In this study, we carry out a practice to detect eye states in facial images. Initially, faces and eyes are detected and extracted from intensity images. After some preprocessing steps, edges of the iris are used to detect a circular structure referring to state of the eyes. The detection of a circular structure is achieved by means of CHT.

Proposed Method

Our proposed method for eye state detection is composed of three steps. Viola-Jones [START_REF] Viola | Rapid Object Detection using a Boosted Cascade of Simple Features[END_REF] cascade object detector which gives higher recognition rates on real time object detection has been used for face and eye detection states. Eye regions are estimated sequel to extraction of faces and eyes from given images. CHT is applied to the images consisting of eye region at the end, in order to detect eye states. BioID face database [8] which consists of 1521 gray scale images that are taken from 23 different subjects under varying circumstances is used in our study.

Face and Eye Detection

Cascade classifier model which is proposed by Ojala T. [START_REF] Timo | Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[END_REF] was executed on 1010 images that provide adequate lighting levels. Face locations were correctly identified on 916 images. Eyes are searched within those locations by the help of Viola-Jones object detection framework just like as face detection. Cascade classifier model which proposed by Marco [START_REF] Marco | ENCARA2: Real-time detection of multiple faces at different resolutions in video streams[END_REF] is used for detecting eye pairs. To detect each eye separately; cascade classifier model which proposed by Shiqi [12] is used. Since our method is based on the intensity of dark region formed by iris and pupil, employing an eye image with the absence of eyebrows became more suitable for detection purposes. Therefore, we employ eye pair images for eye detection instead of separately detected eye images for their minimal eyebrow containment. Cascade classifier was performed on total of 916 images. Eye pairs were detected successfully in 899 images.

Eye State Detection

In order for an eye to carry out seeing task, a black point which is called pupil has to receive light. Lights received from pupil are focused on to rearmost layer of the eye which is called retina layer. Image that is formed on the retina layer is transmitted to visualization center of the brain via optical nerves and thus visual process completes. Iris and pupil are two circles that share the same center. In accordance with this relation half opening of iris means pupil is also half open and so seeing is probable. We have used this information in our study to assume an eye as open. Our system starts with equalizing contrast of the acquired eye image with the histogram equalization. As a result of this process, iris and pupil, which have a lower intensity value compared to sclera (white of the eye) are going to be easily separated from the sclera and the rest of the eye. Afterwards, gray-scale thresholding is applied to the resulting eye image. With the aid of thresholding, iris and pupil had become greatly separated from the rest of the eye. Even so, some unwanted features such as shades, eyelashes and stains could may have reside on the image. Therefore we removed those little residues after obtaining thresholded image. Yet another great factor that hampers our method is the lights which are reflected from the pupil. These lights are forming a high intensity area (brights) inside the low intensity area which is formed by pupil and iris. As this white area has the ability to misdirect our edge detection output, it has been dealt with morphological techniques. After all of these processes, iris and pupil are completely extracted from the rest of the eye and are ready to be served into next step. Edges of the concerned image are detected with use of canny edge detector. Afterwards, presence of circular shapes are searched with the aid of circular Hough Transform. 492 eye images were used for eye state detection. Those images are selected within 899 eye pairs that are obtained from the previous step and they meet the minimum resolution case for applicability of CHT. In 473 of the images eyes were open and in the other 19 of the images eyes were closed. Open and closed eyes are classified with %94.5 and %63.2 accuracy respectively.

Experimental Results

In this section, we will analyze our proposed method by means of error matrix, correct classification rate and kappa statistics. Afterwards, we will present some test results that are obtained with our proposed method.

Confusion Matrix

Confusion matrix is a tool to measure the performance of a classification system. Each row of the matrix shows number of occurrences of an estimated class, while each column of the matrix shows the occurrence of a real class. A confusion matrix is shown in Table 1, which is used to classify a two class system. As stated above; TP (resp. TN) represents the number of instances of class a (resp. class b) well classified by the system. P=TP+FP (resp. N=FN+TN) represents the total of estimated occurrences of class a (resp. class b). p=TP+FN (resp. n=FP+TN) represents the total of real occurrences of class a (resp. class b). T is the sum of occurrences of both classes.

Correct Classification Rate

Correct classification rate (CCR) which is also known as success rate, is obtained by dividing the number of correctly classified samples to the number of total samples as shown in Equation 1.

CCR = T P + T N T (1)

Cohen's Kappa Coefficient

Cohen's Kappa coefficient (κ) [START_REF] Fleissa | Large sample standard errors of kappa and weighted kappa[END_REF] measures the agreement between two raters who each classify N items into C mutually exclusive categories. The equation for Cohens Kappa coefficient is shown in Equation 2.

κ = P 0 -P e 1 -P e (2)
Where P 0 is observed agreement proportion that refers to CCR and P e represents random agreement proportion given by Equation 3.

P e = 1 T 2 [(P * p) + (N * n)] (3)
κ coefficient varies between -1 and 1. When it is equal to 1, it is considered that agreement between the observers on the studied system is perfect. Inversely when it is equal to -1, it is considered that there is a total disagreement on the studied system among the observers. Table 2 shows kappa statistics values along with their interpretations.

Experimental Results

All applications are executed on Matlab software environment by using the system having the following specification: Intel Core2duo T5600 Dual Core Processor clocked at 1.83 GHz, 533 MHz 4 GB RAM. Our software took 0.14 seconds to localize face, 0.06 seconds to localize eye pair and cropping eyes, 0.014 seconds for preprocessing steps and another 0.014 seconds for circular Hough transform. Our software took total of 230 ms in order to process a frame which means 4 fps. Table 3 shows statistical values of the proposed system. Kappa statistic is found as 0.40 which shows that our system works on a fair agreement level. Eye state detection success rates are given in Table 4. %94,5 of the open eyes and %63,1 of the closed eyes are classified correctly. CCR, which is obtained by dividing correctly classified samples to all samples is calculated as %93.

Conclusion

With the development of computer vision techniques, face detection and eye state detection has become important for many applications. In this study, an eye state detection system has been proposed. In order to detect faces from given images, Viola-Jones face detector has been used. Faces had been detected successfully in 916 of 1010 images. Sequel to face detection, eyes pairs are detected by utilizing Viola-Jones eye pair detector. 899 eye pairs were detected in 916 face images. Right and left eyes are acquired by cropping eye pairs. For eye state detection, contrast of the eye image -which was obtained in the previous stageis increased. Then gray level thresholding is applied to this image. Later, the residual areas which are not used in the detection process are eliminated. Holes inside this new image are filled. After this step, edges are extracted via canny edge detection algorithm. Finally, circular shapes are searched within images by using circular Hough transform. Latest image set was composed of 492 images which contains images that provide proper resolution and illumination for eye state detection process to function. % 94,5 percent of the open eye images and % 63,1 of closed eye images are identified correctly. Gray level images are used in this study. Studies that employ colored images could produce better results. Eye state detection could also be possible in low light environment by using capture devices that utilize infrared rays. Eye state detection problem could be solved more efficiently by applying these types of approaches. Eye state information, is used as input to many applications nowadays. With the development of faster and more accurate eye state detection methods there is no doubt that eye state information could utilize itself in broader utilization.

  Samples to the eye detection classes which are TP, TN, FP and FN are shown at Figure 1. Red circles are indicating a valid retina and pupil are found and marks where they reside.

Fig. 1 .

 1 Fig. 1. Examples to detection classes.

Table 1 .

 1 Confusion matrix of a classification system

	Real Class/Estimated Class A	B	Total
	a	True Positive (TP) False Negative (FN) p
	b	False Positive (FP) True Negative (TN) n
	Total	Positive (P)	Negative (N)	Total (t)

Table 2 .

 2 Kappa statistics values along with their interpretations

	Kappa Statistics Interpretation
	κ > 0.8	Almost Perfect Agreement
	0.8 ≥ κ > 0.6	Substantial Agreement
	0.6 ≥ κ > 0.4	Moderate Agreement
	0.4 ≥ κ > 0.2	Fair Agreement
	0.2 ≥ κ > 0.0	Slight Agreement
	0 ≥ κ	No Agreement

Table 3 .

 3 Statistical values of the system

	TP TN FP FN T CCR Kappa
	447 26 12 7 492 0.93 0.40

Table 4 .

 4 Eye state detection success rates

	Real/Detected Open	Closed	Total
	Open	447(%94,5) 26(%5,5) 473(%96,1)
	Closed	7(%36,9) 12(%63,1) 19(%3,9)
	Total	454(%92,2) 38(%7,8) 492(%100)


Paweª Forczma
email: pforczmanski@wi.zut.edu.pl

Piotr Ab¦d¹

Recognition of Occluded Faces Based on Multi-subspace Classication

Keywords: face recognition, subspace method, dimensionality reduction, PCA

In the paper we investigate a problem of face recognition in uncontrolled environment distorted by occlusion, shadows and other local modications. Such problems are very common for real-world conditions, thus the presented algorithm allows to eliminate them. It is based on dimensionality reduction approach (two-dimensional Karhunen-Loéve Transform) and distance-based classication. We use simple transformations involving face normalization and individual facial regions extraction as a pre-processing. Then, we perform independent recognition of extracted facial regions and combine the results in order to make anal classication. The results of experiments conducted on images taken from 9 publicly available datasets show that a quite simple algorithm is capable of successful recognition without high computing power requirements, as opposite to more sophisticated methods presented in the literature. As it was proved, the presented approach gives signicantly better eciency than a whole image-based recognition.

Introduction

The problem of automatic face recognition continuously attracts scientists interest. The review of literature and closer look at practical implementations show that although it might has been successfully solved, due to its broad characteristic, it is still interesting. This constant interest is motivated by the observation that most of the proposed solutions operate mainly in the very narrow real-world conditions (i.e. in the controlled conditions of imaging, with strictly dened illumination, orientation, pose, expression etc.).

The continuous research in this area is inuenced also by the fact that facial recognition algorithms are to be implemented in the devices having low processing power and very limited resources like smartphones and tablets. Hence, robust solutions as simple as possible, yet without suering from signicant performance degradation are still needed.

One of the most important problems in face recognition is a low quality of input data processed by a typical Face Recognition System (FaReS). It should be noted, that the quality is often degraded by two main types of distortions observed in the face area: variable lighting and occlusions/incompleteness of face portrait.

Several examples of face occlusions or similar local distortions that can be found in practice are presented in Fig. 1. As it can be seen, they include full occlusion of dierent face areas, partial occlusion by an independent object, local intensity changes or total information loss in a limited region of face. Those problems highly degrade the performance of FaReS, thus this is the main reason of lasting interest of researchers [START_REF] Tan | Preprocessing and Feature Sets for Robust Face Recognition[END_REF]. It should be also emphasized, that in practice we have an access to a limited training database, thus we have to solve the facial portrait recognition without extending FaReS database with images having all possible variants of global and/or local distortions (in contrast to setups dened in [START_REF] Tan | Preprocessing and Feature Sets for Robust Face Recognition[END_REF]). The literature review shows also, that recognizing images under occlusion requires a careful selection of invariant features. Apart from many complex geometry-based approaches, there are several simple appearance-based methods that combine a few elementary features to describe an image, like Gabor Features [START_REF] Choras | Symmetry-based Salient Points Detection in Face Images[END_REF] or LBP Features [START_REF] Zhang | Multiscale facial structure representation for face recognition under varying illumination[END_REF]. However they lead to the very complex multi-tier algorithms, which is unusable in low-performance devices.

One of the most popular approaches to face recognition employs Principal Component Analysis (PCA) also known as Karhunen-Loeve Transform (KLT) [START_REF] Tan | Preprocessing and Feature Sets for Robust Face Recognition[END_REF][START_REF] Chen | PCA and LDA in DCT domain[END_REF]69]. There has been many scientic works published in the recent years related to the data dimensionality reduction by means of PCA/KLT, as well as their application to the face recognition problem taking into account the twodimensional characteristics of images. Such algorithms have got dierent names i.e. 2DPCA [START_REF] Yang | Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition[END_REF][START_REF] Nagabhushan | Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an ecient approach[END_REF] or MatPCA [START_REF] Chen | Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA[END_REF]. However, most of them are associated with the processing of two-dimensional data by means of data reduction algorithms performed along one dimension only or by dividing an image into smaller parts and reducing them with help of classical PCA. Moreover, those methods operate on fully visible facial portraits not considering a problem of occlusion and local distortions.

The literature review shows that there is a few attempts to deal with above problems, however they are still not fully solved. An interesting approach was presented by the authors of [START_REF] Storer | Fast-Robust PCA[END_REF]. They address a problem of reconstructing images using so called Fast-Robust PCA. The experimental part proves that it is possible to rebuild missing pixels in images of known class, however it is computationally expensive. The improved method presented in [START_REF] Hosoi | Reconstructing occluded regions using fast weighted PCA[END_REF] proved that it is possible to reconstruct distorted pixels in facial portraits using a modied (faster) PCA approach. That method makes it possible to reconstruct occluded regions of the face using Fast Weighted Principal Component Analysis (FW-PCA). The occluded regions are detected and recursively updated. Unfortunately, the authors do not address the problem of recognition accuracy. The extension of classical PCA, called Lophoscopic PCA, aimed at recognition of images under occlusion was presented in [START_REF] Tarrés | A Novel Method for Face Recognition under Partial Occlusion or Facial Expression Variations[END_REF]. The authors covered only small parts of faces (one part a time) which do not represent real-life conditions. Moreover, the main drawback of above method is a complex and time-consuming calculations (six times slower than a classical PCA).

Another interesting algorithm was presented in [START_REF] Min | Improving the recognition of faces occluded by facial accessories, Automatic Face Gesture Recognition and Workshops (FG 2011)[END_REF]. The authors propose an approach which consists of rst detecting the presence of scarf/sunglasses and then processing the non-occluded facial regions only. The occlusion detection problem is solved using Gabor wavelets, PCA and Support Vector Machines (SVM), while the recognition of the non-occluded facial part is performed using block-based Local Binary Patterns (LBP). The main drawback in this case is the assumption about the presence of sunglasses or the scarf, not taking into account other distortions. Moreover, LBP are not the most optimal face recognition approach. On more interesting approach was presented in [START_REF] Park | Glasses removal from facial image using recursive error compensation, Pattern Analysis and Machine Intelligence[END_REF], however that solution is devoted to a removal of one specic object, namely glasses.

The general disadvantage of above methods is the computational complexity coming from the iterative nature of calculating a reconstructed image or the need of detecting occluded regions. Thus, it is a major drawback when it comes to robust and fast face recognition.

In this paper we focus on a method involving dimensionality reduction approach operating in two-dimensional domain of KLT, as it proved to be very ecient, yet not very computationally expensive, especially in case of currently available computing power. We present an algorithm of solving occlusion problem in the aspect of facial portraits recognition, which is much simpler in comparison to the above presented approaches, yet its eciency is very similar. It employs a template database that does not include images with local distortions. At the stage of preprocessing it uses face normalization and facial regions extraction. At the stage of feature extraction it uses 2DPCA/2DKLT as the only instruments of transformation of original data into the low dimensional space [START_REF] Kukharev | Data Dimensionality Reduction for Face Recognition[END_REF]. Finally, obtained feature vectors are used at classication step (using simple distance metrics).

Processing Algorithm

The main idea behind the recognition algorithm is the observation, that in most cases faces are occluded only to some extent. If a human being is able to recognize such an occluded portrait, a computer also can do it. So we assume the following. If we can successfully recognize most of facial parts, then we can decide about the recognition of the whole face.

Facial portraits have dierent geometrical characteristics, hence it is important to normalize them. Here we assume to have en face portraits. The preprocessing of such images includes orientation normalization (based on eyes positions), face cropping and scale standardization. For the purpose of experiments presented in this paper, the eyes positions were marked manually, hoverer it seems that using some sort of automatic approach (i.e. Viola-Jones detector) may give similar results. The intermediate results of pre-processing are presented in Fig. 2.

Then, the distances between salient points are used to crop individual face parts: forehead, eyes, nose and mouth/chin. Such distances used at this stage are derived from the distance between eyes (a in Fig. 2 The recognition scheme is presented in Fig. 3. The Facial Region Extraction is a block responsible for face region extraction, 2DKLT is a dimensionality reduction stage, C is a comparator (distance metrics) and Result blocks represent individual recognition results. Fig. 3. Recognition scheme Recognition of images is performed in subspaces related to each individual facial region. While the database images were registered in a precise manner (eyes positions were marked manually), test images are cropped in an approximate manner (since there is no guarantee, the eyes are visible). If the eyes are covered or impossible to localize, some dierent salient point detector may be used in order to calculate appropriate proportions (i.e. [START_REF] Choras | Symmetry-based Salient Points Detection in Face Images[END_REF]).

Each region forms a database with reduced features and an information about the individual (owner of this particular part). We employ two-dimensional variant of well-known PCA/KLT approach as it makes it possible to obtain a compact representation of features [START_REF] Kukharev | Data Dimensionality Reduction for Face Recognition[END_REF]. As a distance metrics D k for individual regions (k = 1, 2, . . . 5) we use the Chebyshev distance dened on a vector space where the distance between two feature vectors P and Q is the greatest of their dierences along any (i-th) coordinate dimension:

D k (P, Q) = max i (|p i -q i |).
(

Then we use a 1-Nearest Neighbor approach to nd a class center (average vector for all elements in each class), which is the closest one. Finally we perform a voting on the individual results D 1 , D 2 , . . . D 5 . We use simply the mode average of all votes, hence the majority indicates the class of the input image. Such an approach results in a fast and reliable recognition without high computational overhead.

Another interesting and practically important feature of presented approach is the possibility to compute a level of condence. It is evaluated as a number of unanimous votes to the total number of votes. For example, if the voting results in 3 votes indicating the same class (the rest 2 votes indicate dierent classes), then the level of condence is equal to 3/5. The minimal acceptable condence level is equal to 2/5 (40%). Hence we can evaluate the quality of recognition and give a hint to the human operator about the system's performance.

In the voting scheme, there can occur a special case, when there is an equality situation among the votes, i.e. two of the regions say one identity, and two other regions say another identity. It should be noted, that we do not weight the individual results (we do not consider one part of a face be more important than another). Hence, the system results in a identity that is associated with face parts placed in the upper part of the head. In the future, some more sophisticated solutions could be employed, such as presented in [START_REF] Gokberk | Representation plurality and fusion for 3-D face recognition[END_REF].

Experiments

First, it should be noted, that there is no publicly available large database aimed at testing face recognition under occlusion. One possibility is to use AR database [START_REF] Martinez | The AR Face Database[END_REF] which features frontal view faces with dierent facial expressions, illumination conditions, and occlusions (sun glasses and scarf). However it does not incorporate images with occlusions of dierent type, i.e. by independent objects, strong local shadows etc. Thus, the experiments were performed on a self-created database containing images taken from 9 ocially available sets: Olivetti Research Lab (AT&T) [START_REF]Database of Faces[END_REF] The nal database consist of en face portraits only, thus many original images were excluded. All images were converted to grayscale and re-scaled to 160 × 120 pixels. The dimensions of each facial region extracted from facial portraits are presented in Tab.1. The total number of images is equal to 3445 divided into 365 classes. Each class contains 4 24 images (depending on the source database). The database was split into learning and testing datasest in a random way, so the learning set contains 3106 and the testing set 1695 images (5 distortion variants for each of 339 base images, respectively).

Table 1. Dimensions of images representing face regions and mean images

The testing images were altered (using simulated occlusion) and divided into 5 categories, related to the distortion type. Each category is characterized by the area of distortion, the form of distortion and the mean information loss (in terms of the number of pixels changed in comparison to the original image). The information loss/distortion of each part of a face is presented in Tab. We performed recognition employing 16, 100 and 400 principal components per image region (80, 500 and 2000 PCs per face image, respectively). In order to evaluate the performance of our method, we additionally performed a recognition of whole images employing 100 and 400 PCs. The results of recognition of sample images are presented in Fig. 5, where query means testing image, reconstruction represents image composed of individually recognized regions, result(1) represents our algorithms while result [START_REF] Choras | Symmetry-based Salient Points Detection in Face Images[END_REF] shows recognition by whole image approach. The group of pictures on the left hand side show the results where the accuracies of region-based and whole image-based recognition are equal. On the right hand side of the gure, the results of region-based approach are superior to whole image-based approach. The comparative results are presented in Fig. 6. As it can be seen, the recognition of undistorted faces involving whole images -case (A) -gives the highest accuracy (97.6% vs. 95%). In case of portraits with eyes covered with black rectangle -case (B) -presented method of recognition provides lower rate (83.8% vs. 88.5%). Images covered with independent object -case (C) -give the highest recognition rate for whole-image approach (93.8% vs. 90.9%). The rest two cases (D) and (E), where the information loss is the highest show the true potential of presented method. The recognition rates for these distortion types and region-based processing are equal to 63.1% vs. 56.9% and 44.2%, respectively.

Summary

We presented a novel algorithm of recognizing facial portraits under local distortions (occlusion, shadows, intensity change). It incorporates face normalization, extraction of individual face regions, dimensionality reduction and classication Fig. 6. Comparison of recognition performance for dierent variants of methods in multiple sub-spaces. The nal result is obtained by a voting with a majority principle. The results of experiments performed on large database show the superiority of developed method over traditional approach, mainly in case of large information loss. In case of images not inuenced by above distortions, the recognition rate is similar. Although, we did not investigated the inuence of low light intensity or degraded image quality (in case of low resolution and low sensitivity cameras), we think the presented method based on PCA is able to cope with such problems (as it has been proved in the literature many times before). In future, another extension of 2DPCA may be employed, namely the processing of colour images [START_REF] Forczma«ski | Comparison of Tensor Unfolding Variants for 2DPCA-Based Color Facial Portraits Recognition[END_REF], in order to make the whole approach more robust and capable of distinguishing between facial and non-facial features.

Fig. 1 .

 1 Fig. 1. Real distortion examples in the face area

  ): b = 1.5 * a, c = 1.8 * a, and d = 2 * a.

Fig. 2 .

 2 Fig. 2. Processing stages: original image, orientation normalization, scale standardization, salient point detection, facial areas extraction

  : 400 images (40 individuals, 10 image per class); Bio ID [22]: 1520 images (23 individuals); Nottingham DB [23]: 465 images (71 individuals); JAFFE DB [24]: 213 images (10 individuals); Georgia Tech Face Database [25]: 750 images (50 individuals); Aberdeen DB [26]: 690 images (90 individuals); Caltech Faces 1999 [27]: 450 images (27 individuals); Pain Expression [28]: 599 images (23 individuals); FEI Face Database [29]: 2800 images (200 individuals);

  2. The following categories of distortions are included in the database (the examples of simulated images representing above distortions were presented in Fig. 4): (A) Reference images (with no distortions); (B) Eyes covered; mean information loss: 16%; (C) Face area covered with randomly placed object; mean information loss: 13%; (D) Face area with lower intensity; mean information loss: 28 %; (E) Randomly covered face area; mean information loss: 36 %.

Fig. 4 .

 4 Fig. 4. Distortion examples used in the experiments (B, C, D, and E, respectively)

Fig. 5 .

 5 Fig. 5. Sample recognition results

Table 2 .

 2 Mean information loss in terms of percentage of changed (lost) pixels in each part in comparison to the each part area and the whole image, respectively

	Face part		Distortion category
		B	C	D	E
	Forehead	0 (0)	1.7 (0.6) 9.2 (3.1) 39 (12.9)
	Left eye 72.3 (8.1) 4.1 (0.5) 13.9 (1.6) 22 (2.5)
	Right eye	71 (8) 22.7 (2.6) 48.7 (5.5) 20 (2.3)
	Nose	0 (0) 22.6 (3.7) 44.3 (7.2) 33.8 (5.5)
	Mouth/Chin 0 (0) 20.5 (5.8) 40.2 (11.3) 44.6 (12.5)

Paweª Forczma«ski et al.


Tapalina Bhattasali
email: tapolinab@gmail.com

Rituparna Chaki
email: rituchaki@gmail.com

Nabendu Chaki
email: nabendu@ieee.org

A K Choudhury

Study of Security Issues in Pervasive Environment of Next Generation Internet of Things

Keywords: Internet of Things, Any Paradigm, Smart Objects, Security Issues, IPv6, Cloud Services

Internet of Things is a novel concept that semantically implies a world-wide network of uniquely addressable interconnected smart objects. It is aimed at establishing any paradigm in computing. This environment is one where the boundary between virtual and physical world is eliminated. As the network gets loaded with hitherto unknown applications, security threats also become rampant. Current security solutions fail as new threats appear to destruct the reliability of information. The network has to be transformed to IPv6 enabled network to address huge number of smart objects. Thus new addressing schemes come up with new attacks. Real time analysis of information from the heterogeneous smart objects needs use of cloud services. This can fall prey to cloud specific security threats. Therefore need arises for a review of security threats for a new area having huge demand. Here a study of security issues in this domain is briefly presented.

Introduction

Kevin Ashton, cofounder and executive director of MIT Auto-ID Centre, first introduced the term "Internet of Things" in 1999 [START_REF] Sundmaeker | Vision and Challenges for Realising the Internet of Things[END_REF]. It is an emerging concept where smart objects are equipped with sensors or actuators, tiny microprocessor, communication interface, and power resource. As per Libelium report [START_REF]Libelium Unveils the Top 50 Internet of Things Applications[END_REF], it is estimated that more than 50 billion devices will be accessing the Internet by the year 2020. The tremendous increase in demand makes mapping of resource constrained sensor nodes and Internet a big challenge. The term 'Thing' used in "Internet of Things" can be defined as physical or digital or virtual entity that is capable of being identified [START_REF]Internet of Things in 2020:A roadmap for the future, Workshop report by EPoSS (European Technology Platform on Smart Systems Integration)[END_REF] to integrate heterogeneous data, semantics, and objects. IoT combines several techniques such as RFID,Zigbee,Wi-Fi,3G/4G,embedded devices, sensing devices.

To ensure sensing data availability at any time, at any place,effective processing of large amounts of collected sensing data is necessary in the application areas such as environmental monitoring, weather forecasting, transportation, business, healthcare, military application etc. Combining wireless sensor networks with cloud makes it easy to share and analyze real time sensor data on-the-fly. The issues of storage capacity may be overcome by low-cost cloud computing technique [START_REF] Dash | A Survey on Applications of Wireless Sensor Network Using Cloud Computing[END_REF] [START_REF] Ahmed | Integrating Wireless Sensor Networks with Cloud Computing[END_REF]. For security and easy access of data, it is widely used in distributed and mobile environment. The objective of the integrated sensor-cloud framework is to facilitate the shifting of high volume of sensing data from sensor networks to the cloud computing environment; so that scientifically and economically feasible data can be fully utilized to visualize the concept of next generation Internet i.e. Internet of Things. Figure 1 represents "any" paradigm in the context of IoT. In order to provide anytime, anywhere services, a number of smart sensing objects attached to Internet have to communicate with each other through uniquely assigned identity [START_REF]The Internet of Things 2012 New Horizons[END_REF]. This is where128 bits IPv6 steps in. So that, it can support 2 128 addresses, which is approximately 340 undecillion or 3.4  10 38 addresses.

Fig. 1. "Any" Paradigm in IoT

The design of protocol stack for smart objects must be matched with existing Internet hosts in order to create the extended Internet, which is the aggregation of Internet with the IoT. It is very critical to implement IPv6 in low powered sensing objects. As IPv4 address spaces are already used, existing IPv4 (32 bits) enabled systems need to be upgraded to IPv6 compatible devices. The huge number of heterogeneous devices being connected to one another gives rise to newer security threats. The amount of flexibility and availability of information also leads to hitherto unforeseen security breaches. At present, there is a lack of analyzers for the new threats in the network. Securing IP based ubiquitous sensor network (IPv6 or IPv4-Ipv6) in IoT is of great concern for researchers to meet future market demands and satisfaction. In compari-

Internet of Things

Anywhere Anyhow Any time

Anyone

Any path

Any service

Anything son to IPv4, IPv6 provides simplicity, improved routing speed, quality of service and security. IPv6 brings significant assurance of a higher level of security and confidentiality of the transmitted data. The heterogeneous, resource-constrained and distributed natures of the network make conventional security methodologies inefficient. More research works are required to ensure security, performance and interoperability between next generation IoT and existing Internet. The remainder of this paper is organized as follows. Section 2 introduces the concept of next generation network i.e. IoT. Section 3 illustrates open issues in IoT environment. In section 4, possible security threats are explained briefly. Section 5 gives basic idea about security solutions in IoT environment. Section 6 discusses about a case study in this environment. It is followed by a conclusion in section 7.

Next Generation Internet of Things

IoT is an integrated part of future Internet, which may be termed as next generation network. Ambient intelligence which is hidden in IoT environment supports people in carrying out their everyday activities in an easy and natural way. Radio-frequency identification (RFID) is often seen as essential requirement for serving Internet of Things. If all objects around the environment are equipped with radio tags, they can be easily identified. This technology is very useful in health monitoring applications such as automated monitoring of patient's heart condition. In order to track patients' medical history, RFID chips can be implanted in their body. Internet-connected devices can also directly communicate with the required emergency services when sensed data shows the sign of deterioration in the patient's condition .In this way pervasive environment of smart healthcare technique has the capacity to save lives.

Pervasive computing, which is often similar to ubiquitous computing, is an important field of research leading to the domain of IoT. Tremendous developments in technologies such as wireless communications, mobile computing, wearable computers, sensors, RFID tags have led to the evolution of next generation IoT. The goal of pervasive computing is to create ambient intelligence where devices embedded in the environment always provide services to improve quality of life by hiding underlying technologies. In this pervasive environment, intelligent objects are interconnected to autonomously collect, process and transport data in a cooperative way, in order to adapt IoT concept [START_REF]White Paper: Smart Networked Objects and Internet of Things[END_REF]. It can be said that, IoT is more vulnerable to serious security threats than existing network [START_REF] Sundmaeker | Vision and Challenges for Realising the Internet of Things[END_REF] because it includes various constraint such as low resource objects, heterogeneous nature, open environment deployment, dynamic nature. In the next section, some of the considerable open issues for security implementation in IoT are discussed.

Open Issues in IoT Environment

In IoT environment, the system needs a flexible security mechanism to update it easily according to the requirement. Both the user and the system need a secure access control and authorization mechanism within the limitations imposed by IoT environment. Some of the open issues are as follows.

 IoT creates a heterogeneous environment facilitated by mobility of the objects. It can give rise to inconsistent interpretations of data collected from different domains. Due to distributed and ad-hoc nature, it is open to several unique vulnerabilities whose solutions are unknown. There is no central control that can provide required security features. Therefore burden of the security features may be too large for small and limited capacity objects.  In this surrounding, a secure communication channel is needed along with object authentication to track interacting objects. But request for establishing secure channel is also transmitting through the shared, unreliable wireless medium. Therefore feasible solution for tracking objects through insecure channel is still in the phase of research.  If data is shared with unknown objects, the probability of data security reduces automatically. The exact criteria for trust establishment between communicating parties need to be determined.  To work in a smarter way, pervasive environment needs to deal with users' personal data. But sometimes it poses serious threat to the privacy of the user, especially in the situations where people do not want to disclose their detailed personal information. Therefore an intelligent system should be considered to analyze these issues and to adapt dynamic mechanism. Next section focuses on possible security threats in IoT environment.

Possible Security Threats

Various security challenges can arise in heterogeneous features of IoT [START_REF] Roman | Securing The Internet of Things[END_REF]. There are several known attacks in IoT environment whose solutions are available in the market. But aim of this paper is to consider novel challenges in IoT environment whose feasible solutions are still under research. Most of the security issues such as eavesdropping, false routing, message tampering, unauthorized usage, DoS attack are common with existing internet. But the issues related to specific attacks may be quite different. Some issues like secret extractions, tampering of nodes are more serious in IoT environment. This paper considers that security challenges in IoT may occur from compromised wireless sensor network, usage of novel IPv6 protocol, integrated cloud environment, smart sensing objects. In our previous papers [START_REF] Bhattasali | A Survey of Recent Intrusion Detection Systems in Wireless Sensor Network[END_REF][10] [START_REF] Bhattasali | Sleep Deprivation Attack Detection In Wireless Sensor Network[END_REF], we have already discussed about security related issues in wireless sensor network. Now the main focus is to identify new threats in IPv6 based integrated sensor-cloud environment, which is based on number of smart sensing objects.

Security Attacks related to IP-Enabled Environment

There are several existing threats in IP-enabled environment. Security in IPv6 is almost same as IPv4 security in many ways. IPv6 is normally considered as more secure than IPv4 because version 6 includes the concept of IP Security (IPSec). Beside this, some significant differences exist between IPv4 and IPv6 [START_REF]IPv6 and IPv4 Threat Comparison and Best Practice Evaluation[END_REF]. In the transition period, coexistence of IPv4 However an intruder may achieve port numbers by using fragmentation and can bypass security monitoring devices. An attacker can also cause overload of reconstruction buffers on the target system by sending a large number of small fragments, which forces a system to crash.

ICMPv6 Misuse Attack Some important mechanisms in IPv6 networks, such as neighbor discovery and path MTU discovery, are dependent on some types of ICMPv6 messages. ICMPv6 allows error notification response to be sent to multicast addresses, which can be misused by attacker. An attacker can cause multiple responses targeted at the victim by sending packet to a multicast address.

Routing Header Misuse Attack IP options in IPv4 are replaced with extension headers in IPv6. All IPv6 nodes are capable to process routing headers. It is possible that an intruder sends a packet to a publicly accessible address with a routing header containing forbidden address i.e. address of the victim network. Then the publicly accessible host will forward the packet to a destination address stated in the routing header even though that destination address is filtered.

There are several known and unknown security threats which can arise in IP enabled network. Some of the devastating attacks are discussed here. Next subsection describes security threats of the sensor-cloud environment.

Security Threats of Sensor-Cloud Environment

Sensor-cloud environment can easily compromised by the adversary because of the absence of centralized control [START_REF] Yuriyama | Sensor-Cloud Infrastructure[END_REF]. In this virtual environment, main security issue includes violation of authentication and leakage in communication channel. As sensor-cloud framework is deployed in distributed environment, there may appear several security challenges [START_REF] Kapadia | Opportunistic Sensing: Security Challenges for the New Paradigm[END_REF] [15] [START_REF]Study on Security of Next Generation Network[END_REF] that comes from the following perspectives. Environment of sensor nodes can be compromised or individual sensor can be vulnerable to attacks, whose solutions are available. Information flows within the cloud can be affected by compromised cloud nodes. The cloud client can be infected by malicious code, which can lead to further security breaches within the environment. The communication channels between sensors and cloud and between client and cloud are vulnerable to different types of attacks. Some of the typical attacks in this environment are SPAM over Internet Telephony (SPIT), where an attacker sends bulk unsolicited calls to an enterprise; The spammer attempts to initiate a voice session and then relays a prerecorded message if the callee answers; Denial of Service (DoS), which is an severe issue for any IP network-based service; Service theft, where an unauthorized users get access to the network that results into authentication violation.

In the next subsection, the main focus is on security risks for the smart objects in IoT.

Security Risks for Smart Objects which Form Basis of IoT

In IoT environment, the solutions of some known attacks such as man-in-the-middle attack, eavesdropping, routing attacks are already known. The transmission phase in IoT environment may be vulnerable to man-in-the middle attacks, where it is assumed that no third party is able to sit in between the two communicating entities. In completely automated mechanisms, there is usually no prior knowledge about each other and can not always be able to identify intruder. During transmission between smart objects in a network, it may be susceptible to eavesdropping, either for insufficient protection of communication medium or for use of compromised session key. Routing information in IoT can be spoofed, altered, or replayed. Other known relevant routing attacks include sinkhole attack or blackhole attack, selective forwarding, wormhole attack, sybil attack. Other security threats related to smart sensing objects are as follows [START_REF] Sethi | Security in Smart object Networks[END_REF] [START_REF] Roman | Securing The Internet of Things[END_REF].

Privacy Threat Tracking of object's location and usage may give rise to privacy risk for its users. An attacker can gather information about individual object to find out behavioral patterns of the users. Such information can be distributed to interested parties for marketing purposes. A smart object deployed in the ambient environment can easily be captured by an attacker. Such an attacker may then attempt to extract security information to misuse it.

Firmware Replacement Attack When a smart object is in operation or maintenance phase, its firmware or software may be updated to allow for new functionality or new features. An attacker may be able to exploit such a firmware upgrade by replacing the object with malicious software, thereby influencing the operational behavior of the object.

Cloning of Smart Objects by Untrusted Manufacturer

During the manufacturing process of a smart object, an untrusted manufacturer can easily clone the physical characteristics or security configuration of the object. Cloned object may be sold at a cheaper price in the market, and be able to function normally as a genuine object. In the worst case, a cloned object can be used to control a genuine object. An untrusted manufacturer may also change functionality of the cloned object, resulting in degraded functionality with respect to the genuine object. It can also implement additional functionality with the cloned object, such as a backdoor. During the installation of object, a genuine object may be substituted with a similar variant of lower quality without being detected. The main motivation may be cost savings. Genuine objects can be reselled in order to gain further financial benefits. Another motivation may be to damage reputation of competitors.

After going through different security threats from heterogeneous perspective, next section focuses on possible security solutions in IoT.

Security Solutions in IoT Environment

Security in IoT environment should address the following main issues.

 Enabling smart and intelligent behavior of networked objects.  Preservation of privacy for heterogeneous sets of objects.  Decentralised authentication and trust model.  Energy efficient security solutions.  Proper authentication of the objects within the network.  Security and trust for cloud computing services.  Data ownership.

Security should ensure accurate implementation of confidentiality, integrity, authentication, non-repudiation and access control. Two of the main security issues in the IoT are privacy and confidentiality. Because of the scale of deployment and mobility, the cloud of "things" is hard to control. Cryptographic techniques are useful to protect confidential information stored in the network and to transfer secure messages from one ubiquitous node to another. But there are some gaps between available techniques and requirements. Implementing cryptography into the network of smart objects creates tremendous challenges. Because traditional mechanisms are slow in speed, large in size and consume more power and may fail to provide necessary protection for sensed data. Lightweight Cryptography [START_REF] Zhou | On Cryptographic Approaches to Internet-of-Things Security[END_REF][19] is a current field of research that can meet the constraints set by the use of smart objects. There is no strict criterion for classifying a cryptographic algorithm as lightweight but the basic concept is that cryptography techniques need to work by using minimum amount of essential resources of target objects. It can be categorized into hardware-oriented and softwareoriented. Hardware-oriented techniques are more applicable in areas where main concern is about the size of chip and number of clock cycles required for its execution. Software oriented techniques need to be considered when main focus is on memory (Ram and ROM) requirements, and power consumption. Another type of categorization is symmetric versus asymmetric. Asymmetric cryptographic algorithms offer more security than symmetric, but symmetric technique works well, where authentication and integrity are of prime importance than non-repudiation and confidentiality. This can save additional computational cost and power consumption. Asymmetric ciphers are computationally far more demanding in both hardware and software levels. Finally it is the designer's decision to choose the appropriate techniques based upon the application's requirements along with the constraints carried by them.

In some cases, maximum security can only be achieved by designing an effective intrusion detection system which is not intended to prevent attacks. Instead, their purpose is to provide alert about possible attacks, ideally in time to stop the attack or to mitigate the damage. Researchers have been working for quite some time for designing intrusion detection in wireless sensor network and in IP based network. IoT involves heterogeneous network and thus an integration of techniques is needed. Not much work has been done in the field of IoT environment. There exist some open source IDS systems for IPv4 network. By using software, IDS systems in IPv4 networks, procedure of intrusion detection can be automated. In this, intrusion attempt is recognized and logged by IDS system and alert is generated. There is no freeware IDS software for IPv6 networks. By using packet analyzer tools, an intrusion detection procedure will require an efficient network administrator. Therefore IPv6 supporting IDS system must consider IPv6 protocol specific features.  IPv6 defines a new header format, which must be properly recognized by IDS.  IDS must implement support for IPv6 extension headers and to check the order of extension headers. It is recommended for IDS to discard a packet with an undefined "Next Header" value and to record this as incident.  IDS should be capable of detecting duplicate options of Hop-by-Hop option header or destination options header.  IDS with IPv6 support should also be able to recognize and analyze IPv6 traffic tunneled in IPv4.

Some of the existing works on intrusion detection in sensor network based IoT are mentioned below. Most of the researches are directed to wireless sensor network which is a main component of IoT. The lack of fixed infrastructures and scarce resource make WSN difficult to collect audit data for the entire network. It is more difficult to distinguish false alarms and real intrusions. In one of the previous papers [START_REF] Bhattasali | A Survey of Recent Intrusion Detection Systems in Wireless Sensor Network[END_REF], a survey of recent IDS in sensor network has been presented. In the earlier works, different lightweight hierarchical models [START_REF] Bhattasali | Lightweight Hierarchical Model For HWSNET[END_REF] [START_REF] Bhattasali | Sleep Deprivation Attack Detection In Wireless Sensor Network[END_REF] are proposed for heterogeneous wireless sensor network to detect DoS type attacks. But a sensor node can utilize most of the services offered by traditional IP networks with the help of IP stack. Without a proper security framework, it is not possible to grow towards IoT environment. So our research work currently focuses on this domain. In [START_REF] Amin | A Novel Coding Scheme to Implement Signature Based IDS in IP Based Sensor Networks[END_REF], a dynamic coding mechanism to implement distributed signature based lightweight IDS has been proposed in IP based Ubiquitous Sensor Networks. In [START_REF] Amin | A Novel Intrusion Detection Framework for IP Based Sensor Networks[END_REF], intrusion detection and response system has been proposed considering different types of attacks arise from internet hosts, clients and insider. Main module of the IDS resides on the gateway which supports dual stack-one for analyzing packets from Internet (IPA) and another for analyzing packets from USN (UPA) for detecting attacks. In [START_REF] Fu | An Intrusion Detection Scheme Based on Anomaly Mining in Internet of Things[END_REF], a distributed intrusion detection scheme for IoT has been proposed based on anomaly mining where intrusion semantic is analyzed to distinguish intrusion behaviors from anomalies; since all anomalies are not triggered by malicious intrusion. In [START_REF] Liu | Research on Immunity-based Intrusion Detection Technology for the Internet of Things[END_REF], to detect the security threats in IoT, artificial immune system is applied where detectors evolve dynamically to detect new IoT attacks. Newly detected attacks are combined with the attack information library to alarm the manager of the IoT. The problem on how to consider new class of security threats in IP-enabled IoT is currently a challenging research issue.

Therefore a need of efficient security mechanism specifically tailored for this purpose is inevitable. In the next section, a pervasive environment based real life application of IoT has been briefly discussed to give idea about security implementation in next generation network.

Case Study: Pervasive Environment of Healthcare

A healthcare system based on pervasive environment controls health data in an electronic format EPR (Electronic Patient Record) as compared to the largely paper-based Records [START_REF] Dash | Security in Pervasive Computing[END_REF] [START_REF] Varshney | [END_REF]. As EPRs are kept on networked systems for availability reasons, it is accessible from anywhere and is very easy to copy. Examples include confidential personal data like HIV status, psychiatric records, genetic information etc. The usage of EPRs, imposes new security risks to health data. This may lead to unauthorized access and tampering of sensitive EPRs. The pervasive use of wireless techniques makes it easy for malicious adversaries to launch security attacks. As healthcare systems are designed to assist in medical treatment, security vulnerabilities lead the entire system unreliable, putting patients' lives at risk. Some of the most possible vulnerabilities to this type of healthcare systems include the following.

 In case of emergencies, false alarms may be generated or real alarms may be suppressed by the system.  Alteration of health data of specific patients, leading to incorrect diagnosis and treatment.

Another concern has been significantly increased over privacy issues, relating to electronic health data. Reliable healthcare systems must ensure same level of privacy policy for electronic data as applicable to paper based patient records. The main idea behind securing it is to preserve patient privacy. To ensure this, care needs to be taken to prevent all unauthorized access to EPRs in the system. An important property of a medical system is that patients have a high level of control over deciding who accesses their health information. Two additional issues associated with pervasive healthcare systems are security of wireless communication and physical security of handheld devices. Pervasive healthcare systems make extensive use of wireless communication technologies to communicate health data collected by sensor networks, which have many security vulnerabilities. Portable handheld devices that are used by both patients and caregivers, may store sensitive health information about the patient and cause a serious privacy breach, if stolen or misplaced. Therefore, physical security of the devices also has to be considered.

Recent development of cloud computing allows systems to store all or selective health data in cloud storage and ensures availability with reduced expenditures. In cloud design, data is stored on multiple third party servers where the storage can be accessed on demand. Migrating health data into the cloud offers enormous convenience to healthcare service providers because they do not have to worry about the complexities of direct hardware management. But user privacy preservation and proper access control of health data are growing concern. A contextual patient-centric access policy may be adopted to classify the roles. Security solutions of pervasive healthcare systems may focus on protecting health data from different aspects. In recent years several promising prototypes for wearable health monitoring have started to emerge. These devices are being used for continuous monitoring of patients for a long time. But effective security implementation for this type of pervasive applications is an unexplored area till date.

Conclusion

IoT is the emerging technology of the present age which consists of the analysis of new evolving data from heterogeneous sources for creating a new era of real life applications. In this paper, an effort has been made to study the various security issues coming up in IoT environment. It is obvious that as IoT deals with a large number of objects, the use of wireless sensor network and cloud are almost inevitable. This gives rise to the need for transformation from IPv4 to IPv6 based connectivity to handle the huge number of embedded smart objects. The use of IPv6 increases flexibility at the cost of various known and unknown security challenges. There have been many works on securing the wireless sensor networks already. The main focus in this paper is to study security threats in the new IPv6 enabled network, cloud environment and smart sensing "things" in the context of IoT. Without proper security framework, intelligence in IoT environment may lead to major catastrophe. IoT research and innovation activities need to address security issues to support the growth of a smarter world. The possible solutions such as cryptography or intrusion detection etc. have been studied. The authors have considered a case study of healthcare system as this is definitely going to be maximum benefited in the IoT environment. This field is an emerging domain of research at present. The present survey is aimed at the construc-tion of a secure independent living plan for the aged persons in the society in near future.

  and IPv6 especially creates problem regarding security issues. It is because transition mechanisms provide new, previously unknown possi-bilities of intrusion. There are several transition mechanisms, such as tunneling, dualstack configurations. It is very important to understand security implications of the transition mechanisms in order to apply proper security mechanisms. On dual-stack configuration hosts applications can be targeted by both IPv4 and IPv6 attacks. Tunneling mechanisms may also bring misuse possibilities. Tunneling, especially automatic tunneling can facilitate an intruder to avoid ingress filtering checks. Among two major methods of automatic tunneling, "6 to 4" method encapsulates IPv6 packet directly into an IPv4 packet and "Teredo" method encapsulates IPv6 packet into an IPv4 UDP packet. By misusing 6to4 transition mechanism, a DoS attack can be targeted to IPv6 node, IPv4 node or other 6 to 4 node. In 'Teredo" tunneling, all receiving nodes must allow decapsulation of packets that can be sourced from anywhere. This can be a serious security problem. Addresses within IPv4 and IPv6 headers may be spoofed to be used for Denial of Service (DoS) attacks. In this section, the main focus is on new attacks which may arise in IPv6 or IPv6-IPv4 environment. Some of the specific attacks in IP enabled network are briefly discussed.

Reconnaissance Attack IPv4 reconnaissance attack uses ping sweep and port scan techniques. It can be mitigated by filtering certain types of messages used by an intruder. The default subnet size of an IPv6 subnet is 64 bits, or 2 64 , compared to the subnet size in IPv4 of 8 bits, or 2

8

. This increases the scan size to check each host on a subnet by 2 64 -2 8 (approximately 18 quintillion). So ping sweep and port scan are much more difficult to complete in an IPv6 network. New multicast addresses in IPv6 enable intruder to identify key resources such as routers more easily. Additionally, IPv6 networks are even more dependent on ICMPv6 to function properly. Aggressive filtering of ICMPv6 can have negative effects on network functions. As public services on the network need to be reachable with DNS, adversary can attack at least a small number of critical hosts within the victim network. DNS Server can be easily compromised because of using dynamic DNS mechanisms for large nature of IPv6 addresses and the lack of a strict requirement for Network Address Translation (NAT).

Fragmentation Attack Minimum recommended MTU size for IPv6 is 1280 octets. IPv6 protocol specification does not allow packet fragmentation by intermediary devices. It is recommended to drop all fragments less than 1280 octets unless the packet is the last in the flow.