Subspace Based Discrete Transform Encoded Local Binary Patterns Representations for Robust Periocular Matching on NIST’s Face Recognition Grand Challenge

In this paper, we employ several subspace representations (PCA, UDP, KCFA, and KDA) on our proposed discrete transform encoded local binary patterns (DT-LBP) to match periocular region on a large data set such as NIST’s Face Recognition Grand Challenge (FRGC) ver2.0 database. We strictly follow FRGC EXPERIMENT 4 protocol which involves 1-to-1 matching of 8,014 uncontrolled probe periocular images to 16,028 controlled target periocular images (∼128 million pair-wise face match comparisons). The performance of the periocular region is compared with that of full face with different illumination preprocessing schemes. The verification results on periocular region show that subspace representation on DTLBP outperforms LBP significantly and gains a giant leap from traditional subspace representation on raw pixel intensity. Additionally, our proposed approach using only the periocular region is almost as good as full face with only 2.5% reduction in verification rate at 0.1% false accept rate, yet we gain tolerance to expression, occlusion and capability of matching partial faces in crowds. In addition, we have compared the best stand-alone DTLBP descriptor with eight other state-of-the-art descriptors for facial recognition and achieved the best performance. The two general frameworks are our major contribution: (1) a general framework that employs various generative and discriminative subspace modeling techniques for DT-LBP representation, and (2) a general framework that encodes discrete transforms with local binary patterns for the creation of robust descriptors.