Spike Neural Network Based-Face Recognition

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camera performance and facial position, expression, and features (hairstyle, eyeglasses and beards) further affect performance. Accurate image alignment is ...
Chapter Spike Neural Network Based-Face Recognition ADJOUDJ Réda1, GAFOUR Abdel-Kader1, BOUKELIF Aoued² & LEHIRECHE Ahmed1 1University

of Sidi Bel-Abbès/ Computer Science Department /Evolutionary Engineering and Distributed Information Systems Laboratory Algeria 2University of Sidi Bel-Abbès/ Electronic Department / Digital Signal Processing Laboratory Algeria

1. Introduction It is often useful to have a machine that performs the pattern recognition. In particular, machines which can read face images are very cost, effectively. A machine that reads passenger passports can process more passports than a human being in the same time (Hsu,2002). This kind of application saves time and money, and eliminates the requirement that a human performs such as the repetitive task. Face recognition can be classified into identification and verification. Face identification is the process of determining which registered person provides a given utterance. Face verification is, on the other hand, the process of accepting or rejecting the identity claim of a person (Carrillo,2003; Hsu,2002; Mitchell,1997) . The technologies of face recognition , identification and verification, each has its own advantages and disadvantages and may requires different treatments and techniques. The choice of which technology to use is the application-specific (Adjoudj,2002; Beymer,1996; Carrillo,2003; Rowley,1999). 1.1 How face recognition works At the highest level, all the face recognition systems contain two main modules (Haykin,1999 ; Hsu,2002; Mitchell,1997; Norris,1999; Tian & Kanade,1999 ) : feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the captured image of the face that can be used later to represent each person

(Jain,2007; Nandakumar et al.,2007). Feature matching involves the actual procedure to identify the unknown person by comparing extracted features from his/her photo input with the ones from a set of known persons (Lu & Jain,2007). We will discuss each module in detail in later sections. Although the face authenticity appears to be an easy authenticity method in both how it is implemented and how it is used, and the effectiveness of facial recognition technology is influenced heavily by environmental factors, especially lighting conditions. Variations in camera performance and facial position, expression, and features (hairstyle, eyeglasses and beards) further affect performance. Accurate image alignment is necessary for the leading facial recognition algorithms, which rely on identifying eye positions. As a result, the current facial recognition technology is most effective when used in consistent lighting with cooperative subjects in a mug-shot like position, where hats and sunglasses are removed and everyone looks directly at the camera, one at a time. Attempts, to spoof live-scan facial recognition systems, have been successful (Nandakumar,2007). In one test, trial images were obtained by downloading unprotected reference facial images to a computer and by taking digital pictures of an enrolled person, these images were displayed on a notebook computer monitor and were successfully matched, granting testers access to the system. A video of an enrolled person moving his head slightly left and right also fooled the system (Carrillo,2003; Gamboa et al.,2007; Norris,1999; Maltoni et al.,2003; Nandakumar,2007) .

2. Proposed design The problem of face recognition belongs to a much broader topic in scientific and engineering so called pattern recognition. The goal of pattern recognition is to classify objects of interest into one of a number of categories or classes. The objects of interest are generically called patterns and in our case they are sequences of characteristics vectors called matrix codes that are extracted from an input image of a face using the techniques described in the later section. The classes here refer to individual persons. Since the classification procedure in our case is applied on extracted features, it can be also referred to as feature matching (Daugman ,1997; Duin et al., 2004; Haykin,1999; Jain et al.,2007; Maltoni et al.,2003; Thorpe et al.,1998). This document demonstrates how a face recognition system can be designed by conventional artificial neural network and by another more recent neural network, which is called Spike neural network, the both neural networks are used as matching process. Figure 1 resumes how the proposed recognition system operates.

3.Problem statement An artificial neural network system and a spike neural network system can be designed and trained to recognize the persons of the database that is actually used in articles (Adjoudj et al.,2008 ; Adjoudj & Bouklif,2005 ; Delorme,2001). An imaging system that converts each face image centered in the system’s field of vision is available. The result is that each face image is represented as a 32 by 30 matrix of real values (Image size ~ 32 x 30). Perfect classification of N ideal input images are required, and reasonably accurate classification of noisy images (N is equivalent to a number of distinguish classes of person in each database). The N 960-pixel input images are defined as a matrix of input vectors (Image

size ~ 32 x 30) as demonstrated, (Adjoudj et al.,2008 ; Demuth,1998). The target vectors are also defined with a variable matrix called targets, (Demuth,1998 ; Rowley,1999). Each target vector is an N-element vector with a 1 in the position of the face it represents, and 0’s everywhere else. For example, the face number one is to be represented by a 1 in the first element (as this example is the first face of the database), and 0’s in element two through N (Haykin,1999). Face Image 1

Face Data Bases

Learning Phase

Face Image 2

Feature Extraction

Matching Algorithm by using Artificial / Spike Neural Network

Face Image N

Identification Phase

New/Live Face image

Feature Extraction

No

Classified Face ?

Yes Face Not Identified

Face Identified

Fig. 1. Face recognition system based on the Artificial/Spike Neural Network.

4. Face recognition In less than a few seconds, even on a database of hundreds records, the matrix of pixels generated from a face image is compared to the previous enrolled ones to see if it matches any one of them. The decision threshold is automatically adjusted to the size of the search database to ensure that no false matches occur even when huge numbers of matrixes of faces are being compared with the live one. Some bits in a matrix signify if some data is corrupted (e.g. the image of face has degraded by noise), so that it does not influence the process, and only valid data is compared. Decision thresholds take account of the data amount, and the matching operation compensates for any tilt of the face image. A key advantage of the face recognition is its ability to perform identification using a one-to-all search of a database, with no limitation on the number of face records and no requirement for a user first to claim an identity, for example with a card. Our method uses and experiments two neural

networks, the first one is a conventional artificial neural network, but the second one is more recent neural network (Delorme,2001), which is called a spike neural network, the both neural networks are used to match and perform recognition using a one-to-all search of a database, which is described in more detail next. 4.1 Artificial Neural network, “System 1” The network will receive the 960 real values as a 960-pixel input image ( image size ~ 32 x 30, see figure2 ). It will then be required to identify the person by responding with a Nelement output vector ( for more detail about N see above). A person is represented by the N elements of the output vector ( see figure 2 ).

Image of face, matrix

Artificial Neural Network, Face Recognizer Module Face vector,

Outputs

32 x 30 pixel

960 Elements Input Layer 960 neurals

Hidden Layer Outputs

Output Layer N neurals

Fig.2. Architecture of Neural Network To operate correctly the network should set a 1 in the position of the person being presented to the network. All other values in the output vector should be 0. 4.1.1. Artificial neural network architecture The neural network needs 960 inputs and N neurons in its output layer to identify the faces. The network is a three-layer tang-sigmoid transfer function network, like the one used in articles (Adjoudj et al.,2008 ; Demuth,1998) . The tang-sigmoid transfer function was picked because its output range (0 to 1) is perfect to learn the output Boolean values (Haykin,1999) . The hidden layer has 200 neurons. If the network has trouble learning, then neurons can be added to this layer like demonstrated (Adjoudj et al.,2008). The network is trained to set a 1 in the correct position of the output vector and to fill the rest of the output vector with 0’s. However, noisy input images may not create perfect 1’s and 0’s in the network. After the

network has been trained the output will be passed through the competitive transfer function . This function makes sure that the output corresponding to the face, most like the noisy input image, takes on a value of 1 and all others have a value of 0. The result of this post-processing on the output is used actually in (Adjoudj et al.,2008; Demuth,1998). 4.1.2. Artificial neural networks training. To design a neural network that can handle input image it is best to train the network on ideal images. To do this, the network will be first trained on ideal images until it has a low sum-squared error. Then the network will be trained on 10 sets of ideal and noisy images. Figure 3 shows the example of a face from database (for more information about images database, see section 5):

Fig. 3. Example face image from Database with different kind of noises.

The network is trained on two copies of the noise-free database at the same time as it is trained on noisy images. The two copies of the noise-free database are used to maintain the network’s ability to classify ideal images. Unfortunately, after the training described above the network may have learned to classify some difficult noisy face images at the expense of properly classifying a noise free image. Therefore, the network will be again trained on just ideal face images. This ensures that the network will respond perfectly when an ideal face is presented. Each training is done using back-propagation with both adaptive learning rate and momentum. The face recognition system, with artificial neural network “System 1”, is initially trained for a maximum of 5000 epochs or until the network sum-squared error falls below 0.001. Figure 4 shows the training process of proposed neural network, and goal is fixed at 10-3 or 0.001.

Fig. 4. Training process of Artificial neural network, “system 1”. 4.2. Training with Spike neural network “System 2 ”. Now, we design a new face recognition system that trains without an artificial neural network. But, we use directly a more recent neural network, namely Spike Neural Network (Delorme,2001). In tasks like pattern recognition, the performance of artificial networks is still poor when compared to that of humans. Using a go no-go categorization task in which human subjects had to release a button when they detected an animal in a briefly flashed (20 ms) natural photograph, Thorpe et al. showed that the processing required in such a task could be performed in less than 150 ms (Thorpe et al.,1996). In order to reach high order cortical areas, the retinal information must go through at least 10 processing stages, and knowing the constraint of real neurons, (maximum firing rate usually under 100 spikes per seconds), they proposed that this processing was essentially based on an unidirectional flow of

informations in which, in any given layer, each neuron would rarely generate more than one spike. With one spike per neuron, codes based on neuron discharge rate are questioned. As an alternative to rate based neural codes, there is the temporal coding based on the relative order in which the spikes are produced in consistent with lateral inhibition in the first areas of the visual cortex. Spike neural network can use the latency or the relative latency of neuronal discharges, it means that their order of arrival still attains high performances for image processing with only one spike per neuron. Furthermore, the order coding is contrast independent and can be analysed from a mathematical point of view. Spike neural network is designed to simulate very large networks of asynchronous spiking neurons. Neurons are simulated with a limited number of parameters that includes classic properties like the post-synaptic potential and threshold, and also more novel features like dendritic sensitivity. Spike neural network can be used to simulate networks with millions of neurons and hundreds millions of synaptic weights. Optimization of computation time and the aim of real time computation have been one of the driving forces behind the development of Spike neural network (Delorme,2001). There are some characteristics of Spike neural network as described (Delorme,2001 ; Van Rullen et al.,1999) : • Performs image processing using biological plausible network of neurons. • Simulates millions of integrate-and-fire neurons organized in retino-optical maps. • Connects these neuronal maps using projection files, and regroup common synaptic weights to save memory in order to be able to declare several hundred billions of synaptic connections. • Converts gray level images into lists of spikes (also Spike neural network can perform a variety of preprocessing on the input images). • Implements a complex mechanism for projection between neuronal maps of different sizes. • Implements a supervised learning. • Implements an efficient neuronal Rank-Order-Coding scheme (optional) For these characteristics, we design a new face recognition system that trains with spike neural network. When we tested rigorously the performances of Spike neural network, it could compute roughly 20 million connections per second on a standard desktop computer (PC with 1.7 GHz). This is sufficient to model a network of 400 000 neurons in real time, using a time step of 1 ms (assuming 40 connections per neuron, and an average firing rate of 1 spike per second, a value which is a reasonable estimate for the average firing rate of cortical neurons). Note that time resolution increases have virtually no effect on the computation time, and adding a decay parameter to neurons adds roughly 30-40% in the computation time. At the end, the results of the two face recognition systems ( “ System 1 ” & “ System 2 ”) are compared, (for more details see section 5 and 6). 4.2.1. Spike neural network architecture. To illustrate how Spike neural network can be used, we will describe a multiscale face recognition network which extends the face-localization model described by Van Rullen et al. and Delorme et al. (Delorme et al. ,2001; Van Rullen et al.,1999) , and uses an architecture loosely based on the organization of the primate visual system. Input images are first

analysed by arrays of ON-center and OFF-center cells in the "retina" at three different spatial scales. These cells send spikes to neurons in the next layer which contains neurons tuned for 8 different orientations at each spatial scale. Lateral interactions between cells in this layer were used to improve selectivity, and are similar to those described by Zhaoping & Li (Zhaoping & Li ,1998). A weak shunting inhibition was also included to make the neurons sensitive to the order of activation of their inputs. A third layer in the network contains neurons selective for faces at the three spatial scales. The connections between the level 2 orientation maps and these face-selective units were trained using a set of 200 photographs of faces and a supervised learning procedure which attaches high weights to inputs which are systematically among the first to fire, and progressively smaller weights to later firing inputs. Finally, a fourth layer contains neurons which integrate the information at the three different spatial scales in the previous layer. The simulation is successful in the final map, the neurons fire if a face, at any scale, is present in the input image. The model is clearly not very realistic. For example, no attempt was made to model change in resolution with retinal eccentricity, but the architecture demonstrates how Spike neural network can be used to create quite complex multilayer architectures involving large numbers of units, and it shows how different hypotheses could be tested and integrated easily in a biologically plausible neural network. Figure 5 shows the different layers of Spike neural network architecture.

Image of face, matrix

Face vector,

Spike Neural Network, Face Recognizer Module

Neural Network

160 x pixel

120

Outputs

19200 elements Decision Pattern (V4) (Right eye, left eye, and Level 1 (retina) ON/OFF Pixel High Neurones can act as analogue-latency converters, with more strongly activated neurones firing first. One can also use the order of firing (B > A> H > C > G > D > F > E) as a code. With 8 neurones, there are 8! i.e. 40 320 different possible orderings.

Fig.5. Architecture of system ‘2’ with Spike Neural Network.

5. Experiment and Discussion To evaluate the performance of the proposed two methods, we have collected a large number of face’s images of different persons at different moments using an appropriate sensor (e.g. camera) to form our own Database. The database includes 240 face mug-shots from 40 different subjects (then, in this case: N=40 ). The face images are acquired during different sessions and with different kinds of noise, which provide a challenge to our two systems ( “ System 1 ” & “ System 2 ”). To test and compare the two systems, a face image with noise can be taken and presented to the two systems, and a two other databases of face images can be downloaded and presented to our two systems as data-sets, with the first one named CMU Face Database (Mitchell,1997) , to the best of our knowledge, this is the largest face images of person database available in the public domain. The subject consists of 203 members of the CMU university ( then N=203, for more detail about N see section 3 ). the second one named MIT Database (Carrillo,2003) , with 38 subjects from Massachusetts Institute of Technology (MIT), ( then N=38). These three databases of faces can be presented to the two systems as data-sets (see figure 6). Face Recognition System

Matching Algorithm As “Pattern recognition Module”

Maximum Time of Training

Recognition Rate ( RR ) MIT Database (Carrillo,2003) “ N = 38 ”

Our own database (Adjoudj & Bouklif,2006)

CMU Data-set (Mitchell,1997) “ N = 203 ”

“ N = 40 ” Train Set “ System 1”

Artificial Neural Network

“ System2 ”

Spike Neural

Test Set

Train Set

Test Set

Train Set

Test Set

2 Hour 17 min

94.73% 71.05% 95.00%

77.50%

88.66% 73.89%

45 sec Less than 150 ms

100%

92.10%

100%

95.00%

100%

93.10%

Network Table 1. Recognition results on the person’s face databases (of a three data-sets) on a PC with 1.7 GHz CPU. RR: Recognition Rate. N: a number of distinguish classes of person in each database. Table (1) shows the recognition rate (RR) and the effectiveness of the proposed two systems, for the second system with Spike neural network the best results were obtained for recognition rate and training time. In this case, the system is scored a perfect recognition rate of 92.10 %, 95.00% and 93.10 %, with a maximum training time of 150 ms for the three

databases, compared to the case where the artificial neural network is applied, when the rate was 71.05 %, 77.50 % and 73.89 %, with a maximum training time of 2 hours 17 min 45 s.

For example, here is the same faces from CMU databases (Mitchell,1997) :

Here is the same faces from MIT images (Carrillo,2003):

Fig. 6. Description of image database.

Note that the images were acquired under different conditions and with different kinds of noise (i.e. different synthetic noise such as Gaussian noise, which is used in all our experiment). The original images were collected as grey-level images. For example, in figure 7, here is the noisy face and the face which the recognition system picked correctly:

Fig. 7. The noisy face and the face which the recognition system picked correctly.

6. System performance The reliability of the pattern recognition system is measured by testing the system with hundreds of input face images with varying quantities of noise i-e. synthetic noise. We test the two systems at various noise levels and then graphs the percentage of each system errors versus noise. Noise with mean of 0 and standard deviation from 0 to 0.5 are added to input images. At each noise level 100 presentations of different noisy versions of each face image are made and the two system outputs are calculated. The number of erroneous classifications are then added and percentages are obtained (see figure 8 ).

Fig. 8. The reliability of the two systems. The dashed line (blue dashed line) on the graph shows the reliability of the first system ‘’system1’’ (with artificial neural network as pattern recognition) trained with and without noise. The reliability of the second system ‘’system2’’ trained only without artificial neural network but using directly spike neural network as pattern recognition is shown with a solid line (green line). Thus, training the two systems on noisy input face images of person greatly reduced their errors when they have to classify or to recognize noisy face image of person. The two systems did not make any errors for images with noise of mean 0.00 or 0.05 . When noise of mean (i.e. average of noise) greater than 0.05 is added to the images, the first system begins to make errors, but the second system will start to make errors at 0.20. If a higher accuracy is needed the two systems could be trained for a longer time or retrained with more neural in their hidden layers respectively to the first system. Also, the resolution of the input face images could be increased to say, a 640 by 480 matrix. Finally, the two systems could be trained on input images with greater amounts of noise if greater reliability is needed for higher levels of noise.

7. Conclusion Face recognition is a challenging problem and there are still more works that need to be done in this area. Over the past ten years, face recognition has received substantial attention from researchers in biometrics, pattern recognition, image processing, and cognitive psychology communities. This common interest in face recognition technology among researchers working in diverse fields is motivated both by the remarkable ability to recognize people and by the increased attention being devoted to security applications. Applications of face recognition can be found in security, tracking, multimedia, and entertainment domains. We have demonstrated how a face recognition system can be designed by, firstly, an artificial neural network using face images as inputs, and secondary, by a spike neural network, to capture the important characteristics of face, and to simulate the human visual system. Although, Spike neural network is designed as a tool for modeling biological neural networks, the level of performance obtained is such as, in a variety of tasks, it can perform processing architectures, at least as well and in many cases substantially, better than more conventional image processing and pattern recognition techniques. The levels of performance achieved by the human visual system are, in order of magnitude, better than even the most sophisticated artificial vision systems (Delorme,2001), (Adjoudj,2006). By elucidating the computational principles which make this level of performance possible, it may well be possible not only to demonstrate the power of computational neuroscience as a paradigm for understanding biology, but also reveal the potential of the discipline in diverse areas as machine vision and artificial intelligence. Note that the training process did not consist of a single call as a training function. Instead, the two networks were trained several times on various ideal inputs and noisy face images. In this case training a network on different sets of noisy images forced the network to learn how to deal with noise, a common problem in the real world.

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