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access icon free Low-complexity face recognition using contour-based binary descriptor

Face recognition has become a popular topic due to its applications in security, surveillance and so on. Current local methods such as the local binary pattern (LBP) or local derivative pattern (LDP) perform better than holistic methods since they are more stable on local changes such as misalignment, expression or occlusion, but their high computational complexity limit their applications. While LBP is a good feature method, the scale invariant feature transform (SIFT) is widely accepted as one of the best features to capture edge or local shape information. However, SIFT-based schemes are sensitive to illumination variation. Thus, the authors propose an LBP edge-mapped descriptor that uses maxima of gradient magnitude points. It accurately illustrates facial contours and has low computational complexity. Under variable lighting, experimental results show that the authors' method has a 16.5% higher recognition rate and requires 9.06 times less execution time than SIFT under FERET fc. Besides, when applied to the Extended Yale Face Database B, the authors' method outperformed SIFT-based approaches as well as saving about 70.9% in execution time. In uncontrolled conditions, their method has a 0.82% higher recognition rate than LDP histogram sequences in the Unconstrained Facial Images database.

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