A Comparative Analysis of Local Pattern Descriptors for Face Recognition

Abstract

Face recognition has lot of challenges in biometrics. The challenges are addressed effectively by local pattern descriptors. The idea of local descriptors is to determine the feature vector and then compute the difference between test images with training images by using similarity measure. Based on the observation, local approaches attained better performance rate than other approaches in face recognition. Due to that, researchers made a significant attention on local descriptors for face recognition. For nonlinear subspace, the local descriptors will achieve better result than holistic approach. Local pattern descriptor follows simple procedure to extract the facial features. The steps are face alignment, face representation, and matching. Face alignment is the first step of local descriptor, which is used to divide the image into several blocks. Face representation is used to extract the meaningful information from each region. This local feature extraction method carries discriminant information of the region; it will improve the classification rate and matching rate. Local descriptors extract the discriminant information from the neighbors by setting a threshold value as center pixel value, and it is not capable of extracting the detailed information from microstructure. Finally, matching by classification techniques or distance measure is used to identify or verify the person. The local pattern descriptors are more robust against pose, lighting, and scale variations. This chapter describes the various local pattern descriptors and shows the effectiveness of the descriptor. The results of local pattern descriptors are experimented on standard benchmark databases such as FERET, Extended Yale-B, ORL, CAS-PEAL, LFW, JAFFE, and Cohn–Kannade.