A Comprehensive Comparative Study of Handcrafted Descriptors in Face and Palmprint Recognition

For different applications, various handcrafted descriptors are reported in the literature. Their results are satisfactory concerning the application they were proposed. Furthermore in the literature, the comparative study discusses these handcrafted descriptors. The main drawback which was noticed in these studies is the restriction of implementation only to single application. This work fills this gap and provides the comparative study of 10 handcrafted for two different applications and these are face recognition (FR) and palmprint recognition (PR). The 10 handcrafted descriptors which are analyzed are local binary pattern (LBP), horizontal elliptical LBP (HELBP), VELBP, robust LBP (RLBP), local phase quantization (LPQ), multiscale block zigzag LBP (MB-ZZLBP), neighborhood mean LBP (NM-LBP), directional threshold LBP (DT-LBP), median robust extended LBP based on neighborhood intensity (MRELBP-NI) and radial difference LBP (RD-LBP). The global feature extraction is performed for all 10 descriptors. PCA and SVMs are used for compaction and matching. Results are done on ORL, GT, IITD-TP and TP. The first two are face datasets and the latter two are palmprint datasets. In face datasets, the descriptor which attains the best recognition accuracy is DT-LBP and in palmprint datasets, it is MB-ZZLBP which surpass the accuracy of the other compared methods.