The enhanced face recognition using binary patterns of Gabor features

This paper addresses a novel algorithm for face recognition using neural networks trained by Image patterns those are achieved from Gabor features. The system commences on convolving some morphed images of particular face with a series of Gabor filter co-efficient at different scales and orientations. Two novel contributions of this paper are: contribution of morphing and using binary patterns of the Gabor features of those morphed images as an advancement of image recognition efficiency. The neural network employed for face recognition is based on the Multi Layer Perceptron (MLP) architecture with back-propagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a morphed facial image database with images captured in different illumination conditions.