Exploring feature descriptors for face recognition

How to encode a face is a widely studied problem in both pattern recognition and psychology literatures. Many feature descriptors, Gabor feature, Local Binary Pattern (LBP), and Edge Orientation Histogram, have been proposed. In this paper, we give a comprehensive study of these descriptors under the framework of Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA), compared on three different popular similarity measures and two different feature correspondence strategies: holistic and local. Moreover, we present a new feature descriptor named Multi-Radius LBP, and also propose a combination scheme for the LBP and Gabor descriptor. The experiments on the Purdue and CMU PIE databases demonstrate that 1) an obvious recognition boost of LBP is achieved under PCA+LDA framework compared to the direct NN classification; 2) the LBP and Gabor features are comparable as well as mutually complementary, and the combination of these two descriptors brings a significant improvement in classification capability over single ones; and 3) the Multi-Radius LBP shows to outperform all the state-of-the-art feature descriptors.