Semantic pixel sets based local binary patterns for face recognition

Authors:
Zhenhua Chai;Heydi Mendez-Vazquez;Ran He;Zhenan Sun;Tieniu Tan
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, P.R. China;Advanced Technologies Application Center, Playa, Havana, Cuba;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, P.R. China
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Year:
2012

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Abstract

Feature extraction plays an important role in face recognition. Based on local binary patterns (LBP), we propose a novel face representation method which obtains histograms of semantic pixel sets based LBP (spsLBP) with a robust code voting (rcv). By clustering according the semantic pixel relations before the histogram estimation, the spsLBP makes better use of the spatial information over the original LBP. In this paper, we use a simple rule to use the semantic information. We cluster by the pixel intensity values, which is also invariant to monotonic grayscale changes, and it is in particular very useful when there are occlusions and expression variations on face images. Besides, the proposed representation adopts a new code voting strategy for LBP histogram computation, which makes it more robust. The proposed method is evaluated on three widely used face recognition databases: AR, FERET and LFW. Experimental results show that the proposed method can outperform the original uniform LBP and its extensions.