This paper presents the concept of color space normalization (CSN) and two CSN techniques, i.e., the within-color-component normalization technique (CSN-I) and the across-color-component normalization technique (CSN-II), for enhancing the discriminating power of color spaces for face recognition.
Different color spaces usually display different discriminating power, and the authors’ experiments on a large-scale face recognition grand challenge (FRGC) problem reveal that the RGB and XYZ color spaces are weaker than the I1I2I3, YUV, YIQ, and LSLM color spaces for face recognition. They therefore applied their CSN techniques to normalize the weak color spaces, such as the RGB and the XYZ color spaces, the three hybrid color spaces XGB, YRB and ZRG, and 10 randomly generated color spaces. Experiments using the most challenging FRGC version 2 Experiment 4 with 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show that the proposed CSN techniques can significantly and consistently improve the discriminating power of the weak color spaces. Specifically, the normalized RGB, XYZ, XGB, and ZRG color spaces are more effective than or as effective as the I1I2I3, YUV, YIQ and LSLM color spaces for face recognition. The additional experiments using the AR database validate the generalization of the proposed CSN techniques. The authors finally explain why the CSN techniques can improve the recognition performance of color spaces from the color component correlation point of view. (Published abstract provided)
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