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Enhanced local texture feature sets for face recognition under difficult lighting conditions (2007)
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| Venue: | In Proc. AMFG’07 |
| Citations: | 274 - 10 self |
BibTeX
@INPROCEEDINGS{Tan07enhancedlocal,
author = {Xiaoyang Tan and Bill Triggs},
title = {Enhanced local texture feature sets for face recognition under difficult lighting conditions},
booktitle = {In Proc. AMFG’07},
year = {2007}
}
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Abstract
Abstract. Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult
Keyphrases
face recognition difficult lighting condition enhanced local texture feature set face representation practical face recognition system important bottleneck robust illumination normalization local ternary pattern uncontrolled situation matching metric main contribution local distance transform local binary pattern distance transform essential appearance detail local texture descriptor popular datasets state-of-the-art performance local texture efficient preprocessing chain uniform region lbp ltp
