Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor classification. Maturana, D., Mery, D., & Soto, A. In Proc. of XXVIII Int. Conf. of the Chilean Computer Science Society/IEEE CS Press, 2009.
Paper abstract bibtex 3 downloads 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 un- constrained 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 de- signed 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 con- tribution 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.
@InProceedings{ maturana:etal:2009,
author = {D. Maturana and D. Mery and A. Soto},
title = {Face Recognition with Local Binary Patterns, Spatial
Pyramid Histograms and Naive Bayes Nearest Neighbor
classification},
booktitle = {Proc. of XXVIII Int. Conf. of the Chilean Computer Science
Society/IEEE CS Press},
year = {2009},
abstract = {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 un-
constrained 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 de-
signed 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 con- tribution 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. },
url = {http://saturno.ing.puc.cl/media/papers_alvaro/Final-Daniel-09.pdf}
}
Downloads: 3
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Soto},\n title\t\t= {Face Recognition with Local Binary Patterns, Spatial\n\t\t Pyramid Histograms and Naive Bayes Nearest Neighbor\n\t\t classification},\n booktitle\t= {Proc. of XXVIII Int. Conf. of the Chilean Computer Science\n\t\t Society/IEEE CS Press},\n year\t\t= {2009},\n abstract\t= {Face recognition algorithms commonly assume that face\n\t\t images are well aligned and have a similar pose – yet in\n\t\t many practical applications it is impossible to meet these\n\t\t conditions. Therefore extending face recognition to un-\n\t\t constrained face images has become an active area of\n\t\t research. To this end, histograms of Local Binary Patterns\n\t\t (LBP) have proven to be highly discriminative descriptors\n\t\t for face recognition. Nonetheless, most LBP-based\n\t\t algorithms use a rigid descriptor matching strategy that is\n\t\t not robust against pose variation and misalignment. 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