Color correction using color-flow eigenspace model in color face recognition

JaeYoung Choi; Yong Man Ro

doi:10.1117/12.806105

2 February 2009 Color correction using color-flow eigenspace model in color face recognition

JaeYoung Choi, Yong Man Ro

Proceedings Volume 7251, Image Processing: Machine Vision Applications II; 72510Y (2009) https://doi.org/10.1117/12.806105
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

We propose a new color correction approach which, as opposed to existing methods, take advantages of a given pair of two color face images (probe and gallery) in the color face recognition (FR) framework. In the proposed color correction method, the color-flow vector and color-flow eigenspace model are developed to generate color corrected probe images. The main contribution of this paper is threefold: 1) the proposed method can reliably compensate the non-linear photic variations imposed on probe face images comparing to traditional color correction techniques; 2) to the best of our knowledge, for the first time, we conduct extensive experiment studies to compare the effectiveness of various color correction methods to deal with photometrical distortions in probe images; 3) the proposed method can significantly enhance the recognition performance degraded by severely illuminant probe face images. Two standard face databases CMU PIE and XM2VTSDB were used to demonstrate the effectiveness of the proposed color correction method. The usefulness of the proposed method in the color FR is shown in terms of both absolute and comparative recognition performances against four traditional color correction solutions of White balance, Gray-world, Retinex, and Color-by-correlation.

Citation Download Citation

JaeYoung Choi and Yong Man Ro "Color correction using color-flow eigenspace model in color face recognition", Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510Y (2 February 2009); https://doi.org/10.1117/12.806105

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KEYWORDS

RGB color model

Facial recognition systems

Statistical modeling

Image fusion

Feature extraction

Image processing

Principal component analysis

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