Eigen directional bit-planes for robust face recognition

Lei Lei, Seung-Wook Kim, Won-Jae Park, Dae-Hwan Kim, Sung-Jea Ko

Abstract: A visible image-based face recognition system can be seriously degraded in real-life environments by various factors including illumination changes, expression changes, occlusion, and disguise. In this paper, a novel feature descriptor for robust face recognition, Eigen Directional Bit-Plane (EDBP), is introduced to address these issues. It is observed that Local Binary Pattern (LBP) can be decomposed into 8 directional bit-planes (DBP), each of which represents certain directional information of the facial image. Principal Component Analysis (PCA) is then applied to the DBP space to obtain a more compact feature, the EDBP. For face recognition, the proposed EDBP is integrated into conventional state-of-the-art classification methods. Simulation results demonstrate that classifiers with EDBP outperform those with existing feature descriptors under illumination changes, expression changes, occlusion, and disguise.