Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor classification

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}
}

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