Local Gabor Rank Pattern (LGRP): A novel descriptor for face representation and recognition

The Gabor filters are considered one of the best image representation approaches for face recognition (FR). Researchers have exploited various configurations of Gabor magnitude as well as Gabor phase responses and their modeling with other descriptors. In this paper, we propose a novel face representation approach; Local Gabor Rank Pattern (LGRP), which exploits ordinal ranking of Gabor response images. To take advantage of both magnitude and phase parts, we derive, local Gabor Magnitude Rank Pattern (LGMRP) and local Gabor Phase Rank Pattern (LGPRP) descriptors. We also assign different weights for different LGRPs using variance measure of Gabor coefficients. The descriptors are formed using regional histograms extracted from encoded Gabor filter responses. Furthermore, the LGMRP and LGPRP descriptors are combined within a score-level fusion framework to further improve classification accuracy by maximizing the complementary effect. Extensive experiments on standard face benchmark FERET show that the proposed methods outperform conventional Gabor counterparts as well as other Gabor encoding methods in both constrained and unconstrained FR. The proposed methods also achieve comparable performance to state-of-the-art descriptor based methods in FR.