Weighted Voting of Discriminative Regions for Face Recognition

Abstract

This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.