Face recognition using DWT thresholding based feature extraction with laplacian-gradient masking as a pre-processing technique

Abstract

Face recognition under varying occlusions and lighting conditions is challenging, and exacting occlusion and illumination invariant features is an effective approach to solve this problem. In this paper, we propose two novel techniques viz., DWT Thresholding and Laplacian-Gradient Masking, to improve the performance of a face recognition system.DWT Thresholding is used to extract the approximation coefficients along with the prominent detail coefficients of the 1-dimensional DWT of an image, thereby selecting only relevant features and enhancing face recognition. Laplacian-Gradient Masking is a pre-processing technique which combines the edge detection properties of both the Laplacian and the Gradient operators to create a masked image, containing a well-defined contour of the prominent facial features. The resulting pre-processed image contains the salient edge details of the face and prepares the ground for feature extraction. Experimental results show the promising performance of DWT Thresholding and Laplacian-Gradient Masking for face recognition on ORL, UMIST, Extended Yale B and Color FERET databases.