Face Recognition Using Gabor Filter And Convolutional Neural Network

Abstract

Face recognition involves the extraction of different features of human face and its classification for discriminating it from other persons. Face recognition is quite challenging task because faces are complex, multidimensional visual stimuli and also face recognition rate depends on variations in pose, expression, occlusion, resolution, and illumination. Most of the existing face recognition algorithms give poor performance in the presence of high degree variations in human face images. Hence we propose an approach based on deep learning which uses Gabor filter of feature extraction and Convolution Neural Network for classification in order to improve the performance of face recognition with mentioned variations. The experiments conducted on AT & T database and we attained the efficiency of 89.50%.It is seen that 2.5% improvement in the efficiency as reported in literature. Future work is dedicated to evaluate the performance of proposed algorithm on different dataset with varying illumination and its classification.