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2011 30th International Conference of the Chilean Computer Science Society (2009)
Santiago, Chile
Nov. 10, 2009 to Nov. 12, 2009
ISSN: 1522-4902
ISBN: 978-0-7695-4137-2
pp: 125-132
Face recognition algorithms commonly assume that face images are well aligned and have a similar pose -- yet in many practical applications it is impossible to meet these conditions. Therefore extending face recognition to unconstrained face images has become an active area of research. To this end, histograms of Local Binary Patterns (LBP) have proven to be highly discriminative descriptors for face recognition. Nonetheless, most LBP-based algorithms use a rigid descriptor matching strategy that is not robust against pose variation and misalignment. We propose two algorithms for face recognition that are designed to deal with pose variations and misalignment. We also incorporate an illumination normalization step that increases robustness against lighting variations. The proposed algorithms use descriptors based on histograms of LBP and perform descriptor matching with spatial pyramid matching (SPM) and Naive Bayes Nearest Neighbor (NBNN), respectively. Our contribution is the inclusion of flexible spatial matching schemes that use an image-to-class relation to provide an improved robustness with respect to intra-class variations. We compare the accuracy of the proposed algorithms against Ahonen's original LBP-based face recognition system and two baseline holistic classifiers on four standard datasets. Our results indicate that the algorithm based on NBNN outperforms the other solutions, and does so more markedly in presence of pose variations.
face recognition, local binary patterns, naive Bayes, nearest neighbor, spatial pyramid
Domingo Mery, Daniel Maturana, Álvaro Soto, "Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor Classification", 2011 30th International Conference of the Chilean Computer Science Society, vol. 00, no. , pp. 125-132, 2009, doi:10.1109/SCCC.2009.21
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