A Robust Face Recognition Method Combining LBP with Multi-mirror Symmetry for Images with Various Face Interferences
Face recognition (FR) is a practical application of pattern recognition (PR) and remains a compelling topic in the study of computer vision. However, in real-world FR systems, interferences in images, including illumination condition, occlusion, facial expression and pose variation, make the recognition task challenging. This study explored the impact of those interferences on FR performance and attempted to alleviate it by taking face symmetry into account. A novel and robust FR method was proposed by combining multi-mirror symmetry with local binary pattern (LBP), namely multi-mirror local binary pattern (MMLBP). To enhance FR performance with various interferences, the MMLBP can 1) adaptively compensate lighting under heterogeneous lighting conditions, and 2) generate extracted image features that are much closer to those under well-controlled conditions (i.e., frontal facial images without expression). Therefore, in contrast with the later variations of LBP, the symmetrical singular value decomposition representation (SSVDR) algorithm utilizing the facial symmetry and a state-of-art non-LBP method, the MMLBP method is shown to successfully handle various image interferences that are common in FR applications without preprocessing operation and a large number of training images. The proposed method was validated with four public data sets. According to our analysis, the MMLBP method was demonstrated to achieve robust performance regardless of image interferences.
KeywordsFace recognition (FR) local binary pattern (LBP) facial symmetry image interferences multi-mirror average
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The work was supported by National Natural Science Foundation of China (No. 51305392), Youth Funds of the State Key Laboratory of Fluid Power Transmission and Control (No. SKLoFP_QN_1501), Zhejiang Provincial Natural Science Foundation of China (Nos. LY17E050009 and LZ15E050001), and the Fundamental Rsesearch Funds for the Central Universities (No. 2018QNA4008).
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