Total Variation Models for Variable Lighting Face Recognition

Authors:
Terrence Chen;Wotao Yin;Xiang Sean Zhou;Dorin Comaniciu;Thomas S. Huang
Affiliations:
IEEE;-;IEEE Computer Society;IEEE;IEEE
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2006

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Abstract

In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions.