Face Recognition and Verification Using Artificial Neural Network

Automatic recognition of human faces is a significant problem in the development and application of pattern recognition. In this paper, we introduce a simple technique for identification of human faces in cluttered scenes based on neural nets. In detection phase, neural nets are used to test whether a window of 18x27 pixels contains a face or not. A major difficulty in learning process comes from the large database required for face / nonface images. We solve this problem by dividing these data into two groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. The proposed face recognition technique consists of three parts; preprocessing, feature extraction, and recognition steps. Gradient Vector method is used for facial feature extraction. A face recognition system based on recent method which concerned with both representation and recognition using artificial neural networks is presented. It then evaluates the performance of the system by applying two photometric normalization techniques: histogram equalization and homomorphic filtering. The system produces promising results for face verification and face recognition.  Histogramqualization, homomorphic filtering, gradient vector, neural network. I INDRODCTION The demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. The password and ID card can be easily breached since the password can be divulged to an unauthorized user, and the ID card can be stolen by an impostor. Thus, the emergence of biometrics has addressed the problems that plague the traditional verification methods. Biometric which make use of human features such as iris, retina, face, fingerprint, signature dynamics, and speech can be used to verify a person's identity. The biometrics data have an edge over traditional security methods since they cannot be easily stolen or shared. The face recognition system has the benefit of being a passive, non-intrusive system for verifying personal identity. The proposed face recognition system consists of face verification, and face recognition tasks. In verification task, the system knows a priori the identity of the user, and has to verify this identity, that is, the system has to decide whether the a priori user is an impostor or not. In face recognition, the a priori identity is not known: the system has to decide which of the images stored in a database resembles the most to the image to recognize. The primary goal of this paper is to present the performance evaluation carried out using artificial neural network for face verification and recognition. The remainder of this paper ) Volume 1 – No. 14 24 is organized as follows. Section 2 describes the system process flow and the modules of the proposed face recognition system. Section 3 elaborates the methodology used for the preprocessing, feature extraction, and classification of the proposed system. Section 4 presents and discusses the experimental results and the conclusions are drawn in section 5.