Robust face recognition using color information

Document Type

Conference Proceeding

Publication Date

9-14-2009

Abstract

This paper presents a robust face recognition method using color information with the following three-fold contributions. First, a novel hybrid color space, the RCrQ color space, is constructed out of three different color spaces: the RGB, YCbCrand YIQ color spaces. The RCrQ hybrid color space, whose component images possess complementary characteristics, enhances the discriminating power for face recognition. Second, three effective image encoding methods are proposed for the component images in the RCrQ hybrid color space: (i) a patch-based Gabor image representation for the R component image, (ii) a multi-resolution LBP feature fusion scheme for the Cr component image, and (iii) a component-based DCT multiple face encoding for the Q component image. Finally, at the decision level, the similarity matrices generated using the three component images in the RCrQ hybrid color space are fused using a weighted sum rule. The most challenging Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 shows that the proposed method, which achieves the face verification rate of 92.43% at the false accept rate of 0.1%, performs better than the state-of-the-art face recognition methods © Springer-Verlag Berlin Heidelberg 2009.

Identifier

69949181254 (Scopus)

ISBN

[3642017924, 9783642017926]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-642-01793-3_13

e-ISSN

16113349

ISSN

03029743

First Page

122

Last Page

131

Volume

5558 LNCS

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