A color facial authentification system based on semi supervised backporpagation neural network

Naouar Belghini, Arsalane Zarghili LTTI laboratory, Faculty of technical sciences

Sidi Mohamed Ben Abdellah University Fez, Morocco

n.belghini@ieee.ma, zarghili@yahoo.fr

Jamal Kharroubi, Aicha Majda LTTI laboratory, Faculty of technical sciences

Sidi Mohamed Ben Abdellah University Fez, Morocco

jamal.kharroubi@yahoo.fr, aicha_majda@yahoo.fr

Abstract—a Backpropagation Neural Network (BPNN) is one of the most used methods in the domain of face recognition. BPNN need supervised training to learn how to predict results from desired data, and through many research and studies, they proof there robustness to do so. In this paper, we propose a hybrid method to achieve face recognition purpose using semi supervised BPNN. The idea is to get the desired output of the network from an exterior classifier and then apply the back propagation algorithm to recognize facial data.

Keywords: All-Class-in-One-Network; back propagation; Face recognition; Neural Network; SOM.

I. INTRODUCTION Face recognition can be defined as the ability of a system to

classify or describe a human face. The motivation for such system is to enable computers to do things like human do and to apply computers to solve problems that involve analysis and classification.

Research on face recognition is one of the most interesting research areas in vision system, image analysis, pattern recognition and biometric technology. In this context, many paradigms were used in the face recognition problem [1][2].

Currently face recognition systems report high performance levels; however achievement of 100% of correct recognition is still a challenge.

The aim of this paper is to perform an efficient facial recognition system combining SOM algorithm and Back Propagation Neural network. It is organized as follows: Section II gives an overview about some relate works using neural net in face recognition. In section III, we present the two approaches using to learn a neural net that are the supervised and unsupervised learning. In section IV, we present our proposed solution for face recognition. Section V gives the experimental results. Finally, Section VI gives a conclusion.

II. RELATED WORKS Neural networks have been widely used for applications

related to face recognition.

D. Bhattacharjee et al.[3] develop a parallel framework for the training algorithm of a perceptron. The Principal Component Analysis uses the entire image to generate a set of reduced Features. Two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition. In experiment, total of 400 images from YALE B database were used. Among them 200 images are taken for training and other 200 images are taken for testing purpose from 10 different classes.

Y.Khalid & Y.Peng[4] illustrates the importance of using color information in face recognition and introduces a new method for using color information in techniques based on multi layer neural network. The proposed network architecture involves the neurons in the input layer to be divided into three groups, each of which is connected to a separate input vector that represents one of the three color channels. The modified MLNN used in the experiments consists of an input layer, two hidden-layers and an output layer, each network is specialized in recognizing the face of one person, so there is only one neuron in the output layer. The pictures are obtained from the Georgia Tech face database. The rate of recognition attends 91.8%.

Khurshid Ahmad et al[5]present a modular co-operative neural network to classify a set of complex input patterns. One component learns to classify the patterns using the primary vectors and another classifies the same patterns using the collateral vectors. The third combiner network correlates the primary with the collateral. The primary and collateral vectors are mapped on a Kohonen self-organising feature map (SOM), and the combiner shares the same learning paradigm, unsupervised learning, and based on a variant of Hebbian networks. Lefebvre & Garcia [6] present a method aiming at quantifying the visual similarity between an image and a class model. They propose to label a Self-Organizing Map to measure image similarity. During the labeling process, each image signature presented to the network generates an activity

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vote for its referent neuron. Facial recognition is then performed by a probabilistic decision rule.

III. SUPERVISED AND UNSUPERVISED NEURAL NETWORKS An artificial neural network (ANN) is an information

processing paradigm that is inspired by the way biological nervous systems process information. It is composed by a large number of interconnected processing elements (neurons). An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. The most commonly used family of neural networks for pattern classification tasks is the feed-forward network, which includes multilayer perceptron and Radial-Basis Function (RBF) networks. Another popular network is the Self-Organizing Map (SOM), or Kohonen-Network, which is mainly used for data clustering.

A. Back propagation algorithm Back propagation is a multi-layer feed forward, supervised

learning network based on gradient descent learning rule. The general idea with the backpropagation algorithm is to use gradient descent to update the weights so as to minimize the squared error between the network output values and the target output values. The update rules are derived by taking the partial derivative of the error function with respect to the weights to determine each weight’s contribution to the error. Then, each weight is adjusted, using gradient descent, according to its contribution to the error. This process occurs iteratively for each layer of the network, starting with the last set of weights, and working back towards the input layer, hence the name backpropagation. The aim is to train the network to perform its ability to respond correctly to the input patterns that are used for training and the ability to provide good response to the input that are similar.

B. SOM classifier algorithm The self-organizing map, or SOM, introduced by Teuvo

Kohonen is an unsupervised learning process which learns the distribution of a set of patterns without any class information. A pattern is projected from an input space to a position (node) in the map. It has been widely utilized in face recognition area[7]. SOM provides a topological ordering of the classes. Similarity in input patterns is preserved in the output of the process. The topological preservation of the SOM process makes it especially useful in the classification of data which includes a large number of classes.

IV. THE PROPOSED FACE AUTHENTIFICATION SYSTEM

A. The neural network architecture: We first calculate the feature extractor vector of the image

database using RGB components. This vector is divided into three groups, each of which is connected to a separate input vector that represents one of the three color channels. Then, each feature vector is fed into the SOM network resulting a

map composed of all winning nodes. We call an unlabeled node a node that contains under than 4 vectors or more than 6 vectors.

Then we translate theses outputs as desired outputs for the BPNN that is used for training and testing recognition purposes as described in the following section. The outputs will be a vector with all elements as zero only except the one corresponding to the pattern that the sample belongs to.

Figure 1. Generation of desired outputs using SOM classifier

Input layer Hidden layer Output layer

Figure 2. Architecture of the BPNN

The overview of our semi supervised learning is as follow:

Figure 3. Schema of the semi supervised BPNN

B. THE RECOGNITION PROCEDURE

Figure 4. The recognition procedure.

Where Y is the vector of the hidden layer neurons, X the vector of input layer neurons and Z represents the output layer neurons. w is the weight matrix between the input and the hidden layer. w0 is the bias on the computed activation of the hidden layer neurons. v is the matrix of synapse connecting the hidden and the output layers, and v0 is the bias on the computed activation of the output layer neurons. The sigmoid activation function is defined by: f(x) =1/ (1+exp (-x)). eout and ehid are the vector of errors for each output neuron and the vector of errors for each hidden layer neuron respectively, and d is the vector of desired output. DLR and DMF are learning rate and momentum factor.

V. EXPERIMENTAL RESULTS The face image database used in our experiments is a

collect of 20 Persons of Face 94, 95, grimace Directory

database [8]. Some samples of images from this database are shown in Fig5. These face images varies in facial expression and motion. Each person is represented by 20 samples, 5 are used for training and the rest for test. Training database Testing database

Figure 5. Sample images of DB[8].

We implement the algorithm described above. We apply SOM algorithm to calculate the desired outputs for each pattern in the training DB. The system is trained with a map of the dimension 9*9, initial random weights, Gaussian neighbourhood function. The labeling process is as follow: We initialize the desired output of all training vectors by -1 value. We consider a class if the number of vectors by node is between 4 and 6(we train by 5 samples per person). If the total number of classes is equal to 20 then we stop the process.

We obtain in this case 20 classes and 4 unlabeled vectors Else if the total number of classes is equal to 19, we consider the 20th class as collection of the rest of unlabeled vectors.

19 classes + 20th class contains 7 vectors If the total number of classes is less than 19, we label the vectors of those 19 nodes and we use a temporary Data base

that contains the rest of vectors i.e all unlabeled vectors. Then we apply the algorithm for the new database with readjustment of the SOM map dimension (4*4). First execution:

. We obtain 15 classes and 27 unlabeled vectors Second execution:

The total number of classes increases to 19(15+4) and as mentioned before we consider the 20th class as collection of the rest of unlabeled vectors (7).

The backpropagation neural network is then used for the

recognition purposes. In our experiments, we use 48 inputs per pattern, 34 hidden neurons, 20 outputs neurons, and the error is set to 0.0009 for stopping condition.

The following Table shows example of the training results:

TABLE I.

classifier Number of unlabeled vectors

Number of misclassified vectors

SOM 2 1 Semi supervised BPNN

0 0

The following curve shows the rate of recognition in the

testing data base:

Figure 6. The rate of recognition using suervised and semi supervised

BPNN

VI. CONCLUSION We have presented a hybrid neural network that incorporates unsupervised and supervised networks, as can be seen from the experiments; the results are satisfying in comparison with the supervised BPNN, we can deduce that the unlabeled vector in the training DB, generally, does not influence the recognition task and due to his generation ability the neural net can even correct some misclassified vectors. Furthermore, intelligent neural system with 3D dimensional data will be studied in the future.

REFERENCES

[1] W. Zhao, R. Chellappa, P.J. Phillips, A. Rosenfeld, “Face recognition a literature survey”, ACM Comput. Surv. December 2003 Issue.

[2] I. Mario, M. Chacon, State of the Art in Face Recognition, In-Teh. Austria, 2009.

[3] D. Bhattacharjee, M. K. Bhowmik, M. Nasipuri, D. K. Basu & M. Kundu, “A Parallel Framework for Multilayer Perceptron for Human Face Recognition” ,International Journal of Computer Science and Security (IJCSS), Volume (3): Issue (6), 2010.

[4] Y.Khalid and Y.Peng, “A Novel Approach to Using Color Information in Improving Face Recognition Systems Based on Multi-Layer Neural Networks”, Northern Illinois University, USA. Recent Advances in Face Recognition, 2008.

[5] K.Ahmad, M.Casey, B.Vrusias and P.Saragiotis, “Combining Multiple Modes of Information using Unsupervised Neural Classifiers”, springer, Vol. 2709, 2003

[6] G.Lefebvre & C.Garcia ,”A Probabilistic Self-Organizing Map for Facial Recognition”, 19th International Conference on Pattern Recognition, Florida, USA, 2008

[7] Q. Chen, Koji Kotani, F.Lee and T.Ohmi , “Face Recognition Using Self-Organizing Maps”, Tohoku University, Japan.

[8] Dr Libor Spacek, Faces Directories http://cswww.essex.ac.uk/mv/allfaces