PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions
Xiaoyang Tan and William Triggs
In: Analysis and Modelling of Faces and Gestures, 20 Oct 2007, Rio de Janeiro, Brazil.


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 illumination conditions\: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:3658
Deposited By:William Triggs
Deposited On:14 February 2008