A comprehensive comparative study of handcrafted methods for face recognition LBP-like and non LBP operators.

Pattern recognition and computer vision fields experienced the proposal of several architectures and approaches to deal with the demands of real world applications including face recognition. They have almost the same structure, based generally on a series of steps where the main ones are feature extraction and classification. The literature works interessted in face recognition problems, insist on the role of texture description as one of the key elements in face analysis, since it greatly affects recognition accuracy. Therefore, texture feature extraction has gained much attention and became a long-standing research topic thanks to its abilities to efficiently understand the face recognition process, especially in terms of face description. Recently, several literature researches in face application proposed new architectures based on pattern description proved by their discriminative power when extracting the feature information from facial images. These advantages combined with an outstanding performance in many classification applications, allowed the LBP-like descriptors to be one of the most prominent texture description method. Given this period of remarkable evolution, this research work includes a comprehensive analytical study of the face recognition performance of 64 LBP-like and 3 non-LBP texture descriptors recently proposed in the literature. To this end, we adopted a face recognition framework composed of four stages: 1) image pre-processing using gamma correction; 2) feature extraction using texture descriptors; 3) histogram calculation and 4) face recognition and classification based on the simple parameter-free Nearest Neighbors classifier (NN). The conducted comprehensive evaluations and experiments on the challenging and widely used benchmarks ORL, YALE, Extended YALE B and FERET databases presenting different challenges, indicate that a number of evaluated texture descriptors, which are tested for the first time on face recognition task, achieve better or competitive compared to several recent systems reported in face recognition literature. [ABSTRACT FROM AUTHOR]