Abstract
This research paper introduces the novel local binary pattern (LBP) variant for face recognition (FR) called as neighborhood and center difference-based-LBP (NCDB-LBP). In NCDB-LBP, the 4 labeled function is proposed to capture the robust features from 3 × 3 pixel window. For each neighborhood position , 2 first-order derivatives are computed, first computed between the adjacent neighborhood and the current neighborhood and the second computed between the center pixel and the current neighborhood. Employing the proposed function between the 2 first-order derivatives (produced from each neighborhood position) eventually results in 4 labeled window. All 8 neighborhoods are then placed in the 1 × 8 pixel window from which the 4 different binary patterns are produced. This concept is performed in both anticlockwise (ac) and clockwise (c) directions, termed as NCDB-LBPac and NCDB-LBPc descriptors. After binary patterns are encoded for each pixel position, the 4 transformed images are produced from ac direction and 4 from the c direction. All the respective directional transformed images are then divided into 3 × 3 subregions for histogram extraction. The combined histograms from all the respective subregions are the entire feature size of the NCDB-LBPac and NCDB-LBPc descriptors. To reduce the feature size, PCA and FLDA are utilized. Finally, classification is performed by SVMs and NN. The proposed FR approach is tested on ORL, GT, JAFFE, Yale, YB and EYB databases. The proposed FR approach achieves encouraging results.
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Karanwal, S., Diwakar, M. Neighborhood and center difference-based-LBP for face recognition. Pattern Anal Applic 24, 741–761 (2021). https://doi.org/10.1007/s10044-020-00948-8
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DOI: https://doi.org/10.1007/s10044-020-00948-8