Color facial authentication system based on neural network

Summary form only give. 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 humans do and to apply computers to solve problems that involve analysis and classification. Face recognition systems require less user cooperation than systems based on other biometrics (e.g. fingerprints and iris), it is one of the most widely investigated biometric techniques for human identification and it can be used in applications such as access control, passport control, surveillance, criminal justice and human computer interaction. Face recognition is a specific case of object recognition. It is not a unique and rigid object. Indeed, Global features are sensitive to variations caused by emotional expressions, illumination, pose and occlusions. Neural networks have been widely used for applications related to face recognition and Backpropagation Neural Network (BPNN) is one of the most widely used methods in this domain. In this paper we present 3 solutions related to neural network for color face recognition. First we introduce learning-based dimension reduction algorithms. In the literature many methods are used to reduce the dimensionality of the subspace in which faces are presented. Recently, Random Projection (RP) has emerged as a powerful method for dimensionality reduction. It represents a computationally simple and efficient method that preserves the structure of the data without introducing very significant distortion. Our focus was to investigate the dimensionality reduction offered by RP and perform an artificial intelligent system for face recognition. According to the experimental results, we conclude that random projection is an optimal method of dimensionality reduction. In the case of our study, obtaining a higher FR rate depends, among others, on the choice of the random projection matrix and the dimension of the feature vector of original data. Seco- dly, we propose a hybrid method to achieve face recognition purpose using semi supervised BPNN. Traditionally, BPNN needs supervised training to learn how to predict results from desired data, the idea of our approach is to get the desired output of the network from an exterior classifier (SOM) and then apply the back propagation algorithm to recognize facial data. Experiments show that the results are satisfying in comparison with the supervised BPNN. Furthermore, we can deduce that the unlabeled vector in the training DB generally does not influence the recognition task and due to its generation ability the neural net can even correct some misclassified vectors. The third study concerns the use of Bhattacharyya distance to calculate the total error of the network. The error function generally used to train the neural network is Mean Square Error (MSE) based on Euclidean distance measure. In the experimental section we compare how the algorithm converge using the Mean Square Error and the Bhataccharyya distance and results indicated that the image faces can be recognized by the proposed system effectively and swiftly.