1 November 2009 Local binary pattern based face recognition by estimation of facial distinctive information distribution
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Abstract
We present a novel approach for face recognition by combining a local binary pattern (LBP)-based face descriptor and the distinctive information of faces. Several studies of psychophysics have shown that the eyes or mouth can be an important cue in human face perception, and the nose plays an insignificant role. This means that there exists a distinctive information distribution of faces. First, we give a quantitative estimation of the density for each pixel in a fronted face image by combining the Parzen-window approach and scale invariant feature transform detector, which is taken as the measure of the distinctive information of the faces. Second, we integrate the density function in the subwindow region of the face to gain the weight set used in the LBP-based face descriptor to produce weighted chi-square statistics. As an elementary application of the estimation of distinctive information of faces, the proposed method is tested on the FERET FA/FB image sets and yields a recognition rate of 98.2% compared to the 97.3% produced by the method adopted by Ahonen, Hadid, and Pietikainen.
© (2009) Society of Photo-Optical Instrumentation Engineers (SPIE)
Bangyou Da, Nong Sang, "Local binary pattern based face recognition by estimation of facial distinctive information distribution," Optical Engineering 48(11), 117203 (1 November 2009). https://doi.org/10.1117/1.3258349 . Submission:
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