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Abstract-This letter presents the first ultra-lightweight transmitter based on electromagnetic emanation to send embedded intellectual properties (IP) identity (ID) quickly and discreetly. The proposed solution is based on a binary frequency shift keying (BFSK) transmitter that ensures an exceptionally high data rate. In addition, we present a coherent demodulation method using slippery window spectral analysis to recover data outside the device. The hardware resources occupied by the transmitter represent less than 0.022% of a 130 nm Microsemi Fusion FPGA. The experimental bitrate of the data transmission is around 500 times higher than the bitrate available for other state of the art spy circuitry using power consumption. In comparison with other works, our proposal goes clearly towards using a spy circuit in an industrial context for IP protection.

Index Terms-IP protection, side channel, electromagnetic emanation analysis.

I. INTRODUCTION

OR digital circuit design the re-use of embedded intellectual properties (IP) is more and more important due to prohibitive cost of ASIC design. Nevertheless the IP business suffers from a lack a security due to the intrinsic form of IPs sales and exchanges. Many dedicated threats target the IP life cycle and result to revenues losses for the IP designers [START_REF] Colombier | Survey of hardware protection of design data for integrated circuits and intellectual properties[END_REF]. The IP threat model includes illegal re-use, illegal sales, cloning (illegal copy) of the IP. The extent of threats targeting IPs is linked to the type of IP: software IPs (typically hardware description language files), firmware IPs (synthesized netlist), and hardware IPs (FPGA bitstream or physical layout).

One of the solutions for the IP designers to protect their intellectual property is to be able to detect the presence of a copy of an IP embedded in a digital device by using IP identification. Works on IP watermarking and IP fingerprinting try to provide the IP identification service. But, most of the time the published solutions are not practical mainly because of the complexity of the watermarking/fingerprinting verification scheme [START_REF] Marchand | IP watermark verification based on power consumption analysis[END_REF]. Efficient Manuscript submitted June 3, 2015. The work presented in this biref was realized in the frame of the SALWARE project number ANR-13-JS03-0003 supported by the French "Agence Nationale de la Recherche"and by the French "Fondation de Recherche pour l'Aéronautique et l'Espace".

L. Bossuet and V. Fischer are with the Laboratoire Hubert Curien, University of Lyon, Saint-Etienne, 42000, France (e-mail: lilian.bossuet@univ-st-etienne.fr, fischer@univ-st-etienne.fr). P. Bayon is with Brightsight, Delft, 2628, The Netherlans. (e-mail: bayon@brightsight.com).

IP identification scheme needs to be contactless, rapid and ultra-lightweight. Up to now, these three characteristics are not available in the state-of-the-art. To meet these requirements, in this letter we propose an ultra-lightweight binary frequency shift keying (BFSK) transmitter to forward IP identity (that could be generated for example by a feedback shift-register or a physical unclonable function [START_REF] Colombier | Survey of hardware protection of design data for integrated circuits and intellectual properties[END_REF]) discreetly using an electromagnetic channel. Such circuit is usually called "spy circuitry". Using the electromagnetic channel, it is possible to contactless check the presence of an IP inside a digital device.

The rest of this letter is organized as follows. In Section 2, we present related works. In Section 3, we detail the proposed electromagnetic communication of data (i.e. IC/IP information). In Section 4, we present a method to analyze the electromagnetic spectrum to BFSK signal demodulation and in Section 5 we present our conclusions.

II. RELATED WORK

Well-known threat in cryptographic engineering is side channel attacks [START_REF] Kocher | Differential Power Analysis[END_REF]. Most of the dynamic characteristics of both hardware and software implementations of cryptographic primitives can be used for side channel analysis: computation time, power consumption, electromagnetic radiation, optical radiation, even the sound produced during computation. These side channels can be used as transmission channels to send intellectual property data from a device or an embedded IP. For example in [START_REF] Marsh | Protecting designs with a passive thermal tag[END_REF], the thermal channel representing a contactless communication was used to transfer information from an embedded tag to a remote receiver. However the embedded thermal tag used in this commercial solution requires a relatively large area (255 Spartan-3 slices). In [START_REF] Ziener | Power Signature Watermarking of IP Cores for FPGAs[END_REF], the authors propose using two shift registers to generate a recognizable signature-dependent power consumption pattern to reveal the IP signature. Power consumption was also used in [START_REF] Becker | Side-channel based watermarks for integrated circuits[END_REF] to communicate the IP identity. To reinforce such work, the authors of [START_REF] Kerckhof | Intellectual Property Protection for FPGA Designs with Soft Physical Hash Functions: First Experimental Results[END_REF] propose using the power supply signal of an IP as a physical hash function for fingerprinting.

Related works can be found also in the malicious hardware field such as hardware Trojans design. Such systems use side channels to forward secret information such as a symmetric cipher key from cryptographic hardware implementation [START_REF] Lin | Trojan Side-Channels: Lightweight hardware Trojans through Side-Channel Engineering[END_REF], [START_REF] Kutzner | TROJANUS: An Ultra-Lightweight Side-Channel Leakage Generator for FPGAs[END_REF], but also to cause or to amplify sidechannel leakage of cryptographic hardware [START_REF] Gallais | Hardware Trojans for Inducing or Amplifying Side-Channel Leakage of Cryptographic Software[END_REF].

Except [START_REF] Marsh | Protecting designs with a passive thermal tag[END_REF], all the related works use power consumption as a communication channel which is not contactless. Unlike the proposed solution, all the related works are not lightweight An ultra-lightweight transmitter for contactless rapid identification of embedded IP in FPGA L. Bossuet, Senior Member, IEEE, P. Bayon, V. Fischer F and rapid as this letter will show.

III. PROPOSED EM COMMUNICATION OF DEEP DATA

Electromagnetic radiation is an efficient side channel since, unlike measurement of power consumption, electromagnetic radiation can be measured locally. One of the main advantages of this side channel is that it is impossible to hide the leak concerning electromagnetic radiation by using a global countermeasure. Moreover the electromagnetic test bench is not expensive (less than US$ 10K without an oscilloscope, which is the most expensive component). Last but not least, a spectral analysis of the electromagnetic radiation provides information on the oscillating structure such as a ringoscillator [START_REF] Bayon | EM leakage analysis on True Random Number Generator: Frequency and localization retrieval method[END_REF]. For all these reasons, we use the electromagnetic channel for our IP identification scheme. To this end, we designed an ultra-lightweight BFSK transmitter which could be activated outside the device by an ID checker or internally by a specific event (e.g. specific input sequence, internal data value, system state). Note that an enable signal is required to reduce the power consumed by the ring oscillator. Moreover, a permanently activated transmitter could be detected more easily by a spectral analysis of electromagnetic emanations of the device and could also cause local heating and premature aging of the chip.

BFSK is one of the common modulation schemes used in digital communication. The binary data are sent using a sinusoidal carrier at two frequency tones f 0 and f 1 , representing high ('1') and low ('0') logic levels. The binary data arriving at the transmitter input at certain bitrates determine the commutation of the tones at the transmitter output. The proposed BFSK transmitter uses a dedicated configurable ring-oscillator, as shown in Figure 1. The configurable ringoscillator is designed using one multiplexor, N+K delay elements, and a feedback chain controlled by a NAND gate for activation of transmission to reduce power consumption. Actually, the transmitter is used only during a short time when the enable signal is high, and it consumes power only during this small piece of time. The power consumption of this transmitter is thus completely negligible.

Input data controls the multiplexor, as shown in Figure 1. When input data is low, the configurable ring oscillator uses N delays and its oscillation frequency is f 0 . When input data is high, the configurable ring oscillator uses N+K delays and its oscillation frequency is f 1 . Since the ring oscillator's oscillation frequency decreases with an increase in the number of delay elements, frequency f 0 is higher than frequency f 1 . These two frequencies have to be selected according to the bandwidth of the electromagnetic analysis platform, which is used to acquire and measure the transmitted signal. The bandwidth of our test bench, which is presented in [START_REF] Bayon | EM leakage analysis on True Random Number Generator: Frequency and localization retrieval method[END_REF], was limited to 100 MHz and 1 GHz by the low-noise amplifier.

The proposed BFSK transmitter was implemented in Microsemi FUSION flash based FPGA (130 nm CMOS technology) containing 600K logic gates (M7AFS600). The device contains 13 824 tiles, each tile can be used to implement one D-flip-flop or one configurable multiplexorbased logic block implementing any 3-input logic function.

The configurable number of delays in the configurable ring oscillator of the proposed BFSK transmitter makes it possible to select precisely the two frequencies f 0 and f 1 using parameters N and K. Fig. 2 shows the configurable ring oscillator frequencies evolution with N ranging from 0 to 4, and K ranging from 1 to 5. According to Fig. 2, f 0 can be chosen between 119 MHz (N=4) and 385 MHz (N=0) and f 1 can be chosen between 70 MHz (N=4, K=5) and 280 MHz (N=0, K=1).

The number of FUSION tiles used by the BFSK transmitter is very low, i.e. from 3 tiles (N=0, K=1) to 11 tiles (N=4, K=5). These values are equivalent to less than 0.022% and less than 0.080% of the total number of tiles included in the targeted 600K-gate FUSION FPGA, respectively. This very small number of tiles is very promising for good dissimulation of the BFSK transmitter inside the sea of gates/tiles. For XILINX and ALTERA SRAM FPGAs the BFSK consumes from 2 4-input-LUT (N=0, K=1) to 10 4-input-LUT (N=4, K=5). To evaluate its size in ASIC implementations, the gate count is estimated using the Virtual Silicon standard cell library based on the UMC L180 0.18 µm 1P6M Logic process (UMCL18G212T3 [START_REF]0.18 μm VIP Standard Cell Library Tape Out Ready[END_REF]). The delay gate count is estimated by the gate count of a standard NOT gate, 0.67 EG, and that of the standard multiplexor, 2.33 EG. The standard NAND gate uses 1 EG. So the number of gates of the whole BFSK transmitter ranges from 4.67 EG (N = 0, K = 1) to 10.03 EG (N = 4, K = 5). Note that one flip-flop requires between 5.33 EG and 12.33 EG to store a single bit [START_REF]0.18 μm VIP Standard Cell Library Tape Out Ready[END_REF].

The small requirement of logical resources of the proposed spy circuitry makes reverse engineering it harder, although not impossible [START_REF] Torrance | The state-of-the-art in semiconductor reverse engineering[END_REF]. Even with recent CMOS technologies, the attacker can reverse engineer ICs using a scanning electron microscope and an automatic tool for circuitry extraction [START_REF] Torrance | The state-of-the-art in semiconductor reverse engineering[END_REF], [START_REF] Subramanyan | Reverse Engineering Digital Circuits Using Structural and Functional Analyses[END_REF]. Nevertheless, the smaller the piece of hardware used for BFSK transmitter the harder it is to detect during reverse engineering. Detection of the transmitter using standard Trojan detection methods [START_REF] Agrawal | Trojan Detection using IC Fingerprinting[END_REF], [START_REF] Jin | Hardware Trojan Detection using Path Delay Fingerprint[END_REF] is not feasible because the transmitter does not change the data path of the circuit and because of the ultra-low signal-to-noise ratio on the electromagnetic channel, as shown in our experimental results below (Section 4).

IV. EXPERIMENTAL RESULTS

The electromagnetic radiation of the device was evaluated using the near-field electromagnetic analysis test bench described in [START_REF] Bayon | EM leakage analysis on True Random Number Generator: Frequency and localization retrieval method[END_REF] with a spectral range limited to 100 MHz and 1 GHz. Standard devices aimed at direct BFSK demodulation cannot be used for these relatively high frequencies. Available integrated BFSK demodulators are limited to a few dozen megahertz. For this reason, we developed a dedicated BFSK demodulation scheme for our needs, in which a spectral analysis of the low noise amplifier output (a component of the test bench) is performed to measure the f 0 and f 1 spectral contribution. The transmitted high (low) level is detected when f 1 spectral contribution is higher (lower) than that of f 0 . Fig. 3 illustrates the spectral analysis of the BFSK transmitter's electromagnetic emission when N = 1 and K = 1, which corresponds to a small transmitter with high frequencies (f 1 =189.2 MHz and f 0 =272.2 MHz). For the spectral analysis, a 16 384 points FFT was computed. This figure presents a zoom (X and Y axis) on the global spectrum of the local EM emanation of the circuit when the BFSK transmitter sends a '1' (in red) and when the BFSK transmitter sends a '0' (in blue).

Notice also that we placed a small antenna in the close vicinity of the ring. The amplitude of the spectral rays at 180, 200, 220, 240, 260, 280 MHz are very high compared to the two spectral rays at 189.2 MHz and 272.2 MHz. However, we have cut the upper part of the spectrum in order to see the two interesting rays (at 189.2 MHz and 272.2 MHz). These frequencies correspond to the spectral contribution of the BFSK frequencies f 0 and f 1 .

Without knowledge of the BFSK parameters, the electromagnetic transmission cannot be easily detected because it cannot be distinguished from spectral noise. The signal-to-noise ratio of the BFSK transmission in Fig. 3 is -135 dB for a 1 GHz bandwidth. Such an ultra-low SNR represents efficient protection against unwanted BFSK transmitter detection via a side channel. However, knowing the N and K parameters, the BFSK designer can calibrate the demodulation (determine the two frequencies) by electromagnetic analysis of the ring oscillators based on the differential spectral analysis according to [START_REF] Bayon | EM leakage analysis on True Random Number Generator: Frequency and localization retrieval method[END_REF].

The spectral contribution of the two BFSK frequencies during transmission (which is limited by the transmitter enable signal) at the low-noise amplifier output is measured to determine the transmitted bit sequence. In order to apply the demodulation technique described here, called coherent demodulation, theoretically the bitstream rate (bitrate) is limited to 0.5 times the frequency difference between f 0 and f 1 . In the case shown in Fig. 3, theoretically the maximum bitrate is 41.5 Mbps (theoretically limited to 53 Mbps when N = 0 and K = 1). The bitrate of best IP identification methods using power consortium in the literature is always less than 1 Kbps even with recent device (i.e. XILINX Virtex V) [START_REF] Becker | Side-channel based watermarks for integrated circuits[END_REF][START_REF] Kerckhof | Intellectual Property Protection for FPGA Designs with Soft Physical Hash Functions: First Experimental Results[END_REF][START_REF] Lin | Trojan Side-Channels: Lightweight hardware Trojans through Side-Channel Engineering[END_REF][START_REF] Kutzner | TROJANUS: An Ultra-Lightweight Side-Channel Leakage Generator for FPGAs[END_REF].

For the coherent demodulation of the electromagnetic radiation, we propose a slippery window spectral analysis. Indeed, overall spectral analysis masks the effects of the nonstationarity of the signal and therefore provides no information about its temporal evolution. Slippery window spectral analysis is a three-dimensional representation of the signal: amplitude, frequency, and time. It requires two quantities Fw, the width of the FFT window frame and the difference Δτ between two frames. For our experiment, we chose Fw equal to 16 384 points (2 14 -point FFT) and Δτ equal to 100 points. For each frame, the FFT provides the software demodulator with the amplitude of signals f 0 and f 1 which enables the demodulator to distinguish between a transmitted 1 or 0.

To illustrate data transmission from the circuit via the EM channel, we used a shift register that stored the following 16bit sequence: "1011110111011100". The clock frequency of the shift register is 500 kHz. When the enable signal of the transmitter is given, the sequence is sent cyclically to the BFSK transmitter, which transmits it via the electromagnetic channel. Fig. 4 gives the result of the coherent demodulation obtained at a 500 Kbps bit rate (500 times more than [START_REF] Becker | Side-channel based watermarks for integrated circuits[END_REF][START_REF] Kerckhof | Intellectual Property Protection for FPGA Designs with Soft Physical Hash Functions: First Experimental Results[END_REF][START_REF] Lin | Trojan Side-Channels: Lightweight hardware Trojans through Side-Channel Engineering[END_REF][START_REF] Kutzner | TROJANUS: An Ultra-Lightweight Side-Channel Leakage Generator for FPGAs[END_REF]), which served as a proof of concept.

V. CONCLUSION

In this letter, we have presented an ultra-lightweight transmitter of IP identity using the electromagnetic side channel for FPGA implantation. Based on a configurable ring oscillator, our solution exploits a BFSK signal to transmit information by way of the electromagnetic channel. By performing a slippery window spectral analysis of the near field electromagnetic emanations captured locally over the BFSK transmitter circuitry, the proposed transmission achieves a high bitrate (theoretically limited to 53 Mbps with a Microsemi Fusion FPGA), which has not been achieved before. Moreover, the transmitter occupies very small area.

For the highest frequency and data rate, our solution requires 4.67 equivalent gates, which it is less than the requirement of a small D-flip-flop. Such a small area makes reverse engineering of the chip very difficult and detection of the transmitter using standard Trojan detection methods is not realistic.

Fig. 1 .

 1 Fig. 1. Architecture of the ultra-lightweight digital BFSK transmitter based on a configurable ring oscillator.

Fig. 2 .

 2 Fig. 2. Evolution of the configurable ring oscillator frequencies (f0 and f1)with N ranging from 0 to 4, and K ranging from 1 to 5.

Fig. 3 .

 3 Fig. 3. Parts (zoom in x-axis and y-axis) of the electromagnetic emanation spectral analysis of the proposed BFSK transmitter when it sends a high level at f1=189.2MHz (in red) and when it sends a low level at f0=272.2 MHz (in blue).

Fig. 4

 4 Fig. 4 Coherent demodulation of the 16-bit code word "1011110111011100" cyclically sent by the BFSK transmitter at 500 Kbps. The top (bottom) graph in blue (red) corresponds to the evolution over the time of the amplitude of the spectral contribution at f1=189.2 MHz (at f0=272.2 MHz).

16-bit sequence 16-bit sequence

ACKNOWLEDGMENT

The work presented in this letter was realized in the frame of the SALWARE project number ANR-13-JS03-0003 supported by the French "Agence Nationale de la Recherche" and by the French "Fondation de Recherche pour l'Aéronautique et l'Espace".


Grégoire Lefebvre
email: gregoire.lefebvre@orange.com

Christophe Garcia
email: christophe.garcia@liris.cnrs.fr

Learning a Bag of Features based Nonlinear Metric for Facial Similarity

This article presents a new method aiming at automatically learning a visual similarity between two images from a class model. This kind of problem is present in many research domains such as object tracking, image classification, signing identification, etc. We propose a new method for facial recognition with a system based on non-linear projection and metric learning. To achieve this objective, we feed a "Bag of Features" representation of the face images into a specific neural network that learns a mapping to a more compact and discriminant representation. This learning process aims at non-linearly projecting the facial features into a reduced space where two images belonging to the same category (i.e. a person) are "close" according to a given similarity metric and "distant" otherwise. The proposed method gives very promising results for face identification in adverse conditions like expression, illumination and facial pose variations. Experimental results give 97% correct recognition rate on the CMU PIE database containing 68 individuals, under vary variable pose and illumination conditions.

Introduction

Due to a large number of possible applications like biometrics, video-surveillance or advanced human-computer interaction, face recognition systems received an increasing interest during the last decade. Lots of approaches have been proposed in the literature [START_REF] Zhao | Face recognition: A literature survey[END_REF], but identifying human faces remains a challenging problem. The main difficulties are due to unconstraint illumination conditions, variable facial expressions and face poses. We can discern three categories of face recognition method: holistic matching methods using the whole face information, facial region-based methods (e.g. eyes, nose and mouth recognition) and "Bag of Facial Features" representation [START_REF] Lefebvre | Facial biometry by stimulating salient singularity masks[END_REF]. In this article, we focus on methods from the last category by assuming that the local biometric description is salient whatever view is considered to cluster the individual faces. For each salient description, we compute the local signal singularities. Classically, a clustering method is then applied before performing a dimension reduction of the feature vectors using a Self Organizing Map (SOM [START_REF] Kohonen | Self-Organizing Maps[END_REF]). A facial feature similarity is then learned with a customized Siamese neural network that performs a non-linear projection of the facial features. This paper is organized as follows. A brief state-of-the-art about face recognition is proposed in section 2. In section 3, we describe our face recognition strategy. Section 4 presents the experimental results with a comparison with standard state-of-the-art face recognition algorithms. Finally, conclusions and perspectives are drawn.

State-of-the-art

Facial similarity is a pattern recognition problem that has to be solved in a high-dimensional non-linear space. Classical approaches propose a dimensional reduction technique to solve the recognition problem in a lower dimensional feature space. In particular, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been deeply studied for face recognition over the last decade. The features encoded by Eigenfaces (using PCA [START_REF] Turk | Eigenfaces for recognition[END_REF]) and Fisherfaces (using LDA [START_REF] Belhumeur | Eigenfaces vs. fisherfaces: Recognition using class specific linear projection[END_REF]) are based on the second order dependencies and these methods are linear projection methods.

In [START_REF] Turk | Eigenfaces for recognition[END_REF], the authors compute after a linearization step the mean vector, among all images, and subtracted it from all the vectors, corresponding to the original faces. The covariance matrix is then computed, in order to extract a limited number of its eigenvectors, corresponding to the greatest eigenvalues. These eigenvectors, referenced as Eigenfaces, describe a low-dimensionality space base. Face images are then projected in this EigenSpace and classically compared using Euclidian or L1 distances. The LDA approach [START_REF] Belhumeur | Eigenfaces vs. fisherfaces: Recognition using class specific linear projection[END_REF] offers an alternative taking into account the individual classes.

PCA is still a preliminary step reducing the input space, and then LDA is applied in order to maximize the between-class covariances, minimizing the within-class ones.

Other techniques relying on LBPH (Local binary Pattern Histograms [START_REF] Ahonen | Face description with local binary patterns: Application to face recognition[END_REF]) or SIFT (Scale Invariant Feature Transform [START_REF] Luo | Person-specific sift features for face recognition[END_REF]) use a local texture-based classification. The facial image is divided into local regions and descriptors are extracted from each region independently. The global classification is then performed by a combination of local and global similarity.

The main disadvantage of these projection techniques is their linearity. Bartlett et al. show in [START_REF] Bartlett | Face recognition by independent component analysis[END_REF] that first-and second order statistics capture information only the amplitude spectrum of an image, discarding the phase-spectrum. And yet the human capability seems driven by the phasespectrum to recognize faces. A further nonlinear solution to the face recognition problem is provided by neural network based approaches. Independent Component Analysis (ICA [START_REF] Bartlett | Face recognition by independent component analysis[END_REF]) is derived from the principle of optimal information transfer through sigmoidal neurons. The main idea is to build a neural network with a neuron for every pixel in the image that captures discriminant features not necessarily orthogonal, exploiting the covariance matrix and considering the high-order statistics. The advantage of neural classifiers over linear ones is that they can reduce misclassifications among the neighborhood classes. For example, Self Organizing Map (SOM) is invariant to minor changes, while Convolutional Neural Networks (CovNets) provide a partial rotation, translation and scaling invariance.

Hereafter, our proposal combines the advantages of local description clustering and neural network to build a very efficient system.

Our Face Recognition System

Facial Identification

Our face recognition system is mainly divided into three steps: facial feature extraction, facial pattern clustering, and facial feature vector classification. The first step detects perceptually relevant points in the face image. The second step computes local feature vectors and clusters each vector into a neural activity histogram built from Self-Organizing Map winning neurons, as described by Lefebvre et al. in [START_REF] Lefebvre | Facial biometry by stimulating salient singularity masks[END_REF]. For the third step, we propose a non-linear mapping strategy based on Siamese network [START_REF] Bromley | Signature verification using a "siamese" time delay neural network[END_REF][START_REF] Chopra | Learning a similarity metric discriminatively, with application to face verification[END_REF] to classify facial feature vectors. These methods are described in the next sections.

Feature Extraction, Description and Clustering

In the literature, feature extraction, image description and data clustering may be performed with numerous methods. Classically, point detectors aim at extracting local image features (e.g. corner detection [START_REF] Harris | A Combined Corner and Edge Detector[END_REF], blob detection [START_REF] Lindeberg | Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention[END_REF], edge and ridge detection [START_REF] Lindeberg | Edge detection and ridge detection with automatic scale selection[END_REF], etc.) while local image descriptors computes invariant features to geometric transforms [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF][START_REF] Lazebnik | Semi-local affine parts for object recognition[END_REF][START_REF] Bay | Surf: Speeded up robust features[END_REF][START_REF] Laurent | A cascade of unsupervised and supervised neural networks for natural image classification[END_REF]. As described in Lefebvre et al. [START_REF] Lefebvre | Facial biometry by stimulating salient singularity masks[END_REF], our system focuses on wavelet analysis to detect salient point and compute singularity descriptors. Using wavelets is justified by the consideration of the human visual system for which multi-resolution, orientation and frequency analysis are of prime importance according to Hoffman et al. [START_REF] Hoffman | The Role of Visual Attention in Saccadic Eye Movements[END_REF]. For clustering all facial feature vectors from all individuals, a global Self Organized Map (SOM) is learned. Even many clustering approaches exists (e.g. centroid models [START_REF] Moody | Fast Learning in Networks of Locally-Tuned Processing Units[END_REF], connectivity models [START_REF] Fernández | Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms[END_REF], fuzzy clustering [START_REF] Nock | On weighting clustering[END_REF], etc.), SOMs allow a relevant selection and learning process in order to capture facial information. The Kohonen model [START_REF] Kohonen | Self-Organizing Maps[END_REF] constructs a neuron lattice in which the topology of the input space is preserved and each neuron is specialized in a stimuli set (i.e. a neighborhood function limits the neurons to respond to a given stimulus).

Let M be the input space and X = x(t), t ∈ {1, 2, . . . , T } be a facial feature vector set with x(t) ∈ M ⊂ d , where t is the time index. Lets assume that M = m i (t), i ∈ {1, 2, . . . , N } is represented by the set of the reference SOM vectors with m i (t) ∈ d , randomly initialized. For each input vector, the best matching unit (BMU) is defined m c (t) where:

c = arg min i x(t) -m i (t) , ∀i = 1, . . . , N (1)
The topologic neuron map is then updated with the following equation:

m i (t + 1) = m i (t) + λ(t)φ (i) c (t) (x(t) -m i (t)) (2)
where λ(t) is the learning rate and φ When the SOM is learned, a "Bag of Features" is composed of the best representative vectors from the learning database. Each identity vector is then described by the stimulation of the SOM winning neurons with the individual facial feature vectors. This SOM activation histogram H is defined by Equation 3 and illustrated in Figure 1.

H[c] = T t=1 m c (t) -x(t) , ∀c = 1, . . . , N (3)

Siamese Neural Networks

In order to overcome the issues of classification in high dimensional spaces (i.e. curse of dimensionality: e.g. the facial feature vector dimension is here the SOM size), a large number of dimensionality reduction techniques have appeared over the last decade. As previously mentioned, one can cite the classical PCA and LDA methods. The first method performs a linear projection in a space of reduced dimensions, where the variances of the original data are maximized. The second method performs also a linear projection and aims at maximizing inter-class variances while minimizing intra-class variances. In order to enhance the robustness of such linear approaches versus non-linear variations of the data, kernel versions of these algorithms have been investigated (i.e. KPCA, KLDA [START_REF] Yang | Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods[END_REF]). Other non-linear manifold learning methods have recently appeared, like Locally Linear Embedding (LLE) [START_REF] Roweis | Nonlinear dimensionality reduction by locally linear embedding[END_REF], Isomap [START_REF] Geng | Supervised nonlinear dimensionality reduction for visualization and classification[END_REF], and Laplacian Eigenmaps [START_REF] Jia | Incremental laplacian eigenmaps by preserving adjacent information between data points[END_REF]. These non-linear dimensionality reduction techniques aim at optimally preserving the local geometry around each data as well as the global structure of the data but we need here to define and learn a visual similarity metric from the data.

Siamese Neural Networks have first been presented by Bromley et al. [START_REF] Bromley | Signature verification using a "siamese" time delay neural network[END_REF] using Time Delay Neural Networks (TDNN) and applied to the problem of signature verification. Siamese neural networks learn a non-linear similarity metric by repeatedly presenting pairs of positive and negative examples, i.e. pairs of examples belonging to the same class or not. The principal idea is to train the neural network to non-linearly map the input vectors into a subspace such that a specific metric in this subspace approximates, not the local geometry like in the methods cited above, but the "semantic" distance in the input space. Two examples of the same category are supposed to yield a small distance in this subspace and two examples of different categories a large distance.

Let us call this mapping G W (X) and its parameters W (i.e. neural weights). Thus, the goal is to learn the parameters W of the function G W (X) such that the similarity metric (see Equation 4) is small if X 1 and X 2 belong to the same class and large otherwise.

E W (X 1 , X 2 ) = G W (X 1 ) -G W (X 2 ) 2 (4)
The choice of G W (X) is arbitrary and, in our approach, is given by a Multilayer Perceptron (MLP). Note that the parameters W are shared by the neural networks( hence the name "Siamese" neural network) and therefore the distance metric is generally symmetric. Figure 2 illustrates the functional scheme of this learning machine.

Similarity Distance

This idea was then adopted by Chopra et al. [START_REF] Chopra | Learning a similarity metric discriminatively, with application to face verification[END_REF] who used Siamese ConvNets in the context of face verification. More precisely, the system receives two face images and has to decide if they belong to the same person or not. The ConvNet allows learning to extract features using a cascade of convolution and subsampling filters. They use the similarity (Euclidean distance) E W that they try to minimize for genuine pairs of facial features and maximize for impostor pairs. One of the problems of this energy function is that, if minimizing E W to zero for genuine pairs is tractable by error backpropagation, on the other hand, one cannot decide which distance should be the target for impostor pairs. The problem could be overcome by minimizing another energy function designed for impostor pairs, L(E W ) where L is a monotonically decreasing function, which is difficult to choose.

In our proposal, instead of extracting features with Conv-Nets, we represent the facial feature vector H extracted from SOM activations (cf. Equation 3). Then, we apply a Siamese one-hidden-layer MLP which projects the input feature vectors H into a vector of smaller dimension G W (H). More formally, given a triplet (H, H + , H -) such that X is a facial feature vector, H + belongs to the same class as H and H -belongs to another class, we would like the scalar product of the similar ones to be higher than that of the dissimilar ones: G W (H).G W (H + ) > G W (H).G W (H -). We therefore define the following objective functions to minimize in order to be able to apply supervised learning via classical backpropagation:

E W (H) = (1 -cos(G W (H), G W (H + ))) 2 +(0 -cos(G W (H), G W (H -))) 2
(5)

Minimizing E W can be interpreted as searching for a projection G W (estimating W in the neural network) such that facial feature vectors H and H + are collinear and facial feature vector H and H -are orthogonal. This choice is also motivated by the better performance of the cosine distance in PCA and LDA space compared to Euclidean distance.

Experimental Results

We evaluated the proposed method on the CMU Pose, Illumination, and Expression (PIE) face database [START_REF] Baker | The cmu pose, illumination, and expression (pie) database[END_REF]. It contains 41,368 images of 68 people, each person being pictured under 13 different poses, 43 different illumination conditions, and with 4 different expressions (c.f. Figure 3). All faces are automatically extracted using the face detector proposed in [START_REF] Garcia | Convolutional face finder: A neural architecture for fast and robust face detection[END_REF] and resized to 200 × 200 pixels. In order to assess the system performances, we use a 3-fold cross validation method in the following experiments. For each fold, the database is divided into three datasets: one third of the images for learning, one third for validation and one third for test. Among the different datasets, we respect the proportion per individual of illumination, expression and pose images. Concerning the non-linear projection with Siamese MLP, experiments have been performed to choose a correct configuration with the facial feature vectors H of size 400. We consider a two-layer MLP architecture with a varying number of neurons in each layer (N h sigmoid neurons in the hidden layer and N o linear neurons in the output layer). For each configuration, the classical backpropagation algorithm is applied on the training set, and stopped when the mean square error between the actual output and the target outputs starts to grow on the validation set (with early stopping strategy), so that overlearning is avoided.

Figure 5 reports the recognition rates obtained for different configurations which have been chosen to perform a non-linear projection into a smaller target space: (N h ,N o )=(300,200), (200,100), (100, 50), (50,25). One can notice that the best recognition result is obtained for (N h ,N o )=(100,50), with corresponds to a MLP Siamese Network with one hidden layer of 100 sigmoid neurons and an output layer of 50 linear neurons. Therefore, in the final scheme, each facial feature vector of size 400 is projected into a 50-dimensional space, where the similarity metric (i.e. the cosine distance) is applied.

For each of the three folds, the trained system is then evaluated on the test dataset. Table 1 presents our results for each fold with a k nearest neighbor classification (here k = 1) directly from the SOM activation histograms H (i.e. SOM-1NN) and after non-linear projection with the Siamese MLP and the application of the cosine similarity metrics (i.e. SOM-SIAM).

For comparison, we have also implemented three standard face recognition methods: Eigenfaces [START_REF] Turk | Eigenfaces for recognition[END_REF], Fisherfaces [START_REF] Belhumeur | Eigenfaces vs. fisherfaces: Recognition using class specific linear projection[END_REF] and Local Binary Pattern Histograms (LBPH) [START_REF] Ahonen | Face description with local binary patterns: Application to face recognition[END_REF] (see section 2).

These results show first that SOM-1NN is efficient gi- ving a correct recognition rate of around 90%. This first result overcomes the classical Eigenfaces, Fisherfaces and LBPH methods. This observation proves that the database is composed of many variations in illumination, expression and viewpoints that linear projection methods can not deal with. The LBPH method also outperforms the Eigenfaces and Fisherfaces approaches. This texture classification is built from predefined facial regions, consequently different viewpoints are difficult to handle. Within the SOM feature extraction, point detection allows us to cluster salient facial information among all views. Moreover, the application of the Siamese MLP in our method SOM-SIAM that learns how to project the facial feature vectors in order to maximize the similarity cosine distance gives a gain of around 7% to reach around 97% of correct recognition rate. These results show that the proposed method is able to handle strong variations in pose, illumination and expression. Nevertheless, the Figure 6 shows some misclassified examples from the CMU PIE database using SOM-SIAM.

Conclusion and Perspectives

In this paper, we have proposed a novel face recognition method using a neural non-linear projection scheme. Based on the two main properties of SOM, which are dimension reduction and topology preservation, this architecture fea-expression illumination light Figure 6. Some misclassified face samples from the CMU PIE database using the method SOM-SIAM. tures all facial identities by neural activity counts. In order to quantify the visual similarity between two face images, a non-linear metric is directly learned, via a Siamese neural network that searches for an optimal projection that clusters facial feature according to "semantic" order instead of geometric distances. The proposed solution gives very promising results on a difficult face dataset. As an extension of this work, we plan to develop a strategy to optimally learn the hyperparameters of the proposed system related to the architectures of the SOM and the Siamese MLP.

Figure 1 .

 1 Figure 1. SOM activation histogram.

Figure 2 .

 2 Figure 2. A Siamese Neural Network.

Figure 3 .

 3 Figure 3. Some face samples from the CMU PIE database with different poses, illumination conditions and expressions. For each face image of size 200 × 200 pixels, the facial feature vectors are extracted from 1000 local signatures, clustered in a squared SOM composed of N s x N s neural units. Each facial feature H is therefore the activation results of the SOM and is of size N s x N s . The best configuration, i.e. the choice of N s , is determined by a 3 cross-validation on the training and validation databases, as shown in Figure 4. As shown by this figure, the best result of 89.85% is obtained with N s = 30, but a good compromise between correct recognition rate and feature vector

Figure 4 .

 4 Figure 4. Mean face recognition rates and standard deviations (bars) for different SOM size configurations.

Figure 5 .

 5 Figure 5. Face recognition rates with different Siamese MLP configurations.

Table 1 .

 1 Recognition rates, means and Standard Deviations (SD) on the CMU PIE database.

	Methods	Fold 1	Fold 2	Fold 3	Mean Standard Deviation
	Eigenfaces	54.65% 55.97% 59.22% 57.60%	(2.30)
	Fisherfaces 87.26% 81.19% 83.63% 84.03%	(3.05)
	LBPH	89.30% 89.97% 89.97% 89.75%	(0.39)
	SOM-1NN 90.35% 89.03% 90.17% 89.85%	(0.72)
	SOM-SIAM 97.27% 96.32% 97.07% 96.89%	(0.50)


Hohenstaufen 1 was aware of this truth and therefore entrusted his better jurist (Pier della Vigna) to formulate the laws ruling the Studium Neapolitanum, that is, the first modern State University [START_REF] Vv | Enciclopedia Federiciana Ist. Naz. Encicl[END_REF][START_REF] Verger | Les universite´s au Moyen Age[END_REF]. Based on some considerations that cannot be completely presented in a limited space and that therefore are further developed by dell'Isola [START_REF] Dell'isola | A difficult problem for artificial intelligence: how to assess originality of scientific research and the dangers of apostrophes in family names[END_REF], 2 we claim that the models needed to describe all phenomena related to scientific creativity (and, in particular, suitable to detect effectively its manifold manifestations) have to be, in fact, very sophisticated. This is to be expected, as the process of creating original and predictive scientific theories is manifestly the most complex ever investigated by the human mind. Moreover, it has always been suspected that when a rational mind starts to investigate its own functioning processes some paradoxical phenomena could arise, making vain the whole effort of self-analysis. One should expect that correspondingly complex models must be used to model human minds and to compare the efficiency of two or more human minds and eventually to establish which one is most suitable, for example for a occupying a given tenured position. In mathematical physics one of the most complex state space structures that is used is given by Fre´chet Metric Manifolds. We conjecture that in order to establish a metric (in the sense of topology) in the set of the minds of all active scientists no simpler concept can be used. We could use for the imagined Fre´chet Manifold the following name: Manifold of Human Intelligence. We go further to conjecture that a given position profile could be characterized by means of a point (or a compact subset) of the Manifold of Human Intelligence, and that the system in charge of the selection process could be asked to find the candidate that minimizes his distance from such a ''target'' set. For sure such a process needs the development of a very deep and complex theory and the design of very advanced artificial intelligence systems. 3 This complexity seems to have been forgotten as in recent decades, instead of reasonably complex mathematical objects, it has been proposed to describe creativity, originality and inventiveness by means of the simplest mathematical objects that are available for modelling phenomena: natural numbers. Indeed, more and more often the different candidates for any position or research grant are evaluated and compared by means of so-called bibliometric indices. We claim that bibliometric indices cannot be used at all to evaluate the scientific quality of researchers, chair or grant holders; indeed, they are not a reliable measure of the independence and originality of the minds of the scientists with which they are associated. At most bibliometric indices are a measure of the social skills and popularity of the single researcher or of the managers of an academic institution. Moreover, the software programs used for calculating bibliometric indices are affected by some evident flaws. In particular, they are not able to handle family names, including apostrophes: the damage inflicted to individuals having this kind of family names is enormous and may also infringe the fundamental principle established by the International Covenant on Civil and Political Rights [START_REF]UN General Assembly resolution 2200A (XXI) of 16[END_REF] about the right of having a name.

The range of applicability of democratic vote and its limited applicability in some decision processes

In the first forms of democracy (Athens and the Republican Rome), the delicate decision related to the guiltiness or innocence sentencing in criminal processes was entrusted to relatively large popular bodies. In Athens the average size of a jury was 501 members, while in the Republican period in Rome a jury constituted between 30 and 70 members, very often chosen by lot. It has been verified that such large bodies cannot too often arrive at a fair decision. Indeed, the populistic and political dynamics that arise inside them and the limited time in which they must arrive at a decision both represent unsurmountable limits. In the Anglo-Saxon juridic system peer juries constituted by 12 or at most 15 members were introduced, 4 with a composition ruled by laws that assure the ''distance'' between the jurors and all persons involved in the criminal process. The formation of a jury is often done through a selection by lot. Experience has shown that, in this way, the optimal decision system for assuring both fair judgements and democratic control of judiciary (or selecting) system is obtained. 5 The jury must be large enough to prevent the affirmation of strong personalities and small enough to prevent the onset of populistic group dynamics. Moreover, for small committees it is possible to impose a procedure that lasts a sufficiently long period of time to ponder carefully the decisions before passing to the final majority verdict. We claim that choosing professors and grant holders or evaluating single scientists or academic institutions by means of bibliometric indices is equivalent to entrusting large popular bodies for the selection or evaluation procedures. This choice is inducing very dangerous degenerations, allowing for frauds and, in general, producing a decrease of the quality of academic bodies. Those who support the use of bibliometric indices (because they believe that, in this way, the abuses occurred in the decision of peer selecting committees can be avoided) must admit that the limits of the system based on ''academic jury'' formation cannot be reduced by replacing these juries with an algorithm based on bibliometrics indices. Indeed, bibliometric indices can (and are) manipulated by the coordinated actions of networks of the authors of scientific papers. Moreover, when using bibliometric indices it is nearly impossible to establish who selected a certain chair or grant holder. Instead, the appointment of a jury is always clearly establishing a public link between the decision maker and the selected person.

3. To stop frauds in the decision process based on the appointment of academic juries is simpler than avoiding frauds in the citation mechanisms Indeed, it is possible to conceive and carefully design procedures, similar to those used for forming juries for criminal processes, to rule the formation and functioning of the peer committees entrusted to nominate professors, chair and research grants holders. On the other hand, although they are based on some deep and useful intuitions, Bibliometrics and Scientometrics are yet too young as disciplines; therefore, it is not possible yet, by means of the theoretical insight gained thanks to them, to design new procedures that are better and more effective than those based on academic juries. In order to replace the old, well-established and most efficient procedures it will be necessary to wait for the development of Turing machines that are able to pass a more complex version of the Turing test. In this, a group of human scientists must try to discern between one of their colleagues and a suitably designed expert Turing machine. Such a machine, once having passed such an enhanced Turing test (and under the control of a strong and stable ''fairness'' and ''honesty'' algorithm) will be able (possibly) to replace human juries. This will happen only when game and artificial intelligence theories reach such a level of development that a decision-making process based on their results will become more reliable than that based on randomly chosen sets of well-qualified humans.

The thesis which is put forward here is as follows:

bibliometric indices cannot replace peers evaluation and selection committees

The development of the web and the increase of computing machine speed supplies to scientists a further powerful tool: the virtual total availability of all published papers and textbooks and the possibility of consulting them in real time from every internet connected computing machine. The dream of the founders of the Library of Alexandria seems close to be fully realized. By using suitable software and appropriate search keywords every knowledge already acquired by science will be soon available under the fingertips of every scientist (if the prices of commercial publishers are not be too high). Undoubtedly, this possibility will contribute to an acceleration in the accumulation and organization of human knowledge. However, as with every tool conceived by the human mind, the uses of such a powerful tool are not all leading only to positive and desirable effects. Indeed, the recent possibility of easily calculating any kind of bibliometric index induced some serious distortions in the structure of academic institutions. These distortions can have very serious consequences in the capability of the future generations of scientists to produce original, creative and effective research. Indeed, the more and more popular tendency to base evaluation of scientific papers, scientists, groups of scientists and whole universities on the calculation of some bibliometric indices is transforming scientific research into a social game whose aim is simply to get more citations. The enormous increase of published papers, citation counting and automatic evaluation of scientific quality tools is distracting too many creative scientists from the formulation of carefully pondered theories and is, instead, inducing them to indulge in the dangerous philosophy whose motto is ''publish or perish''. The claim of this editorial is that the evaluation the scientific quality of researchers, professors and scientific projects cannot be obtained by means of the mere calculation of some simple natural numbers. Instead, as done up to few years ago since the foundation of the first universities, this evaluation process must be entrusted to a peer committee, that is, an academic jury. As the process of selecting a new professor (or of assigning a munificent research grant) is very expensive for the whole society it must be carefully controlled; therefore, it should be ruled in a similar way as a criminal process aimed to establish the guilt or innocence of a citizen. Selecting peer committees (academic juries) must be, however, formed following carefully designed legal rules and constituted exactly as it is done for juries. The interested reader is referred to detailed discussion of this thesis in dell'Isola [START_REF] Dell'isola | A difficult problem for artificial intelligence: how to assess originality of scientific research and the dangers of apostrophes in family names[END_REF], Robinson [6], Stergiou and Tsikliras [START_REF] Stergiou | Global university rankings uncovered: introduction[END_REF], Stergiou and Lessenich [START_REF] Stergiou | On impact factors and university rankings: from birth to boycott[END_REF] and Taylor et al. [START_REF] Taylor | Rankings are the sorcerer's new apprentice[END_REF].

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Conflict of interest statement

None declared.

Notes

1. He was the heir of the Kingdom of Sicily and Sacred Roman Emperor, one of the most enlightened Sovereigns of Italian history and the grandson of Frederich Barbarossa (Red-Beard in Italian), that is, the first Emperor who protected the freedom of the newly established Universitas Bononiensis (Authentica Habita or Privilegium Scholasticum Friderici I). 2. Where the ancient form of Juvenal-style satire is used to reinforce the rational arguments presented here and, in more detail, there. In other words, the cited paper tries to redress degenerated customs by ridiculing them (as suggested by the motto: Castigat Ridendo Mores). 3. Such an enormous technological progress could be closer than we can now imagine: consider that recently an intelligent machine could get a relatively high score in a Turing test (greater than one third). 4. It is suggested to recall: (i) The Magna Charta Libertatum, where the principle of peer judgement was established, was given in 1215; (ii) The Studium Neapolitanum was founded in 1224; and (iii) Pier Della Vigna was sent as ambassador in England in 1235. 5. Recently, in Italy and for a short period, the university selecting committees were formed using a choice by lot among all professors working in the whole country: the results of the action of the so formed committees were so surprisingly of high quality that the Italian Parliament changed the law immediately.