Face Recognition Using Gabor Wavelet Transform and Feed Forward Neural Network

In recent years, an explosion in research on pattern recognition systems using neural network methods has been observed. Face Recognition (FR) is a specialized pattern recognition task for several applications such as security: access to limited areas, banking: identity confirmation and identification of wanted people at airports. Biometric techniques deals with identifying individual with the help of their biological data. Face plays an important role in conveying identity and emotion. People can recognize thousands of faces learned throughout their lifetime and identify familiar faces at a glance even after every years of separating. In this paper we will explain about the tasks involved in face recognition and outline a complete Face Recognition System (FRS) based on Gabor transform and Feed Forward Neural system (FFNN). Face recognition system includes two stages: training and testing series. We use Gabor wavelet transform for function extraction and then Feed Forward Neural Network as a classifier. Gabor transform is used to extract features at grid points and graph matching for the proper positioning of the grid. Neural Network is used for the purpose of classification because it is a mathematical model which solves the problem of a group of highly connected neurons to realize compositions of non linear functions. Experimentation is carried out on Face Recognition System (FRS) by using Yale Face Database and Olivetti Research Laboratory (ORL) database.