Share Email Print
cover

Proceedings Paper

Color correction using color-flow eigenspace model in color face recognition
Author(s): JaeYoung Choi; Yong Man Ro
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 3 February 2009
PDF: 12 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510Y (3 February 2009); doi: 10.1117/12.806105
Show Author Affiliations
JaeYoung Choi, Information and Communications Univ. (Korea, Republic of)
Yong Man Ro, Information and Communications Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

© SPIE. Terms of Use
Back to Top