Learning Discriminative Local Binary Patterns for Face Recognition. Maturana, D., Mery, D., & Soto, A. In 9th IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2011.
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Paper abstract bibtex 17 downloads
Paper abstract bibtex 17 downloads Histograms of Local Binary Patterns (LBPs) and variations thereof are a popular local visual descriptor for face recognition. So far, most variations of LBP are designed by hand or are learned with non-supervised methods. In this work we propose a simple method to learn discriminative LBPs in a supervised manner. The method represents an LBP-like descriptor as a set of pixel comparisons within a neighborhood and heuristically seeks for a set of pixel comparisons so as to maximize a Fisher separability criterion for the resulting his- tograms. Tests on standard face recognition datasets show that this method can create compact yet discriminative descriptors.
@InProceedings{ maturana:etal:2011,
author = {D. Maturana and D. Mery and A. Soto},
title = {Learning Discriminative Local Binary Patterns for Face
Recognition},
booktitle = {9th IEEE International Conference on Automatic Face and
Gesture Recognition (FG)},
year = {2011},
abstract = {Histograms of Local Binary Patterns (LBPs) and variations
thereof are a popular local visual descriptor for face
recognition. So far, most variations of LBP are designed by
hand or are learned with non-supervised methods. In this
work we propose a simple method to learn discriminative
LBPs in a supervised manner. The method represents an
LBP-like descriptor as a set of pixel comparisons within a
neighborhood and heuristically seeks for a set of pixel
comparisons so as to maximize a Fisher separability
criterion for the resulting his- tograms. Tests on standard
face recognition datasets show that this method can create
compact yet discriminative descriptors.},
url = {http://saturno.ing.puc.cl/media/papers_alvaro/FG-2011.pdf}
}Downloads: 17
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Soto},\n title\t\t= {Learning Discriminative Local Binary Patterns for Face\n\t\t Recognition},\n booktitle\t= {9th IEEE International Conference on Automatic Face and\n\t\t Gesture Recognition (FG)},\n year\t\t= {2011},\n abstract\t= {Histograms of Local Binary Patterns (LBPs) and variations\n\t\t thereof are a popular local visual descriptor for face\n\t\t recognition. So far, most variations of LBP are designed by\n\t\t hand or are learned with non-supervised methods. In this\n\t\t work we propose a simple method to learn discriminative\n\t\t LBPs in a supervised manner. The method represents an\n\t\t LBP-like descriptor as a set of pixel comparisons within a\n\t\t neighborhood and heuristically seeks for a set of pixel\n\t\t comparisons so as to maximize a Fisher separability\n\t\t criterion for the resulting his- tograms. 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