Pose-robust face recognition with Huffman-LBP enhanced by Divide-and-Rule strategy

Abstract Face recognition in harsh environments is an active research topic. As one of the most important challenges, face recognition across pose has received extensive attention. LBP feature has been used widely in face recognition because of its robustness to slight illumination and pose variations. However, due to the way of pattern feature calculation, its effectiveness is limited by the big rotations. In this paper, a new LBP-like feature extraction is proposed which modifies the code rule by Huffman. Besides, a Divide-and-Rule strategy is applied to both face representation and classification, which aims to improve recognition performance across pose. Extensive experiments on CMU PIE database, FERET database and LFW database are conducted to verify the efficacy of the proposed method. The experimental results show that our method significantly outperforms other approaches.