Color space construction by optimizing luminance and chrominance components for face recognition

In recent face recognition techniques utilizing the color information, researchers tried to select one conventional color space or learn a color space from the given training data to achieve better performance. RQCr, DCS and ZRG-NII color spaces have gained reputation as effective color spaces in which the face recognition performs better than in the others. However, at the moment, how to construct effective color spaces for face recognition has not been thoroughly studied. In this paper, we propose a color space LuC1C2 based on a framework of constructing effective color spaces for face recognition tasks. It is composed of one luminance component Lu and two chrominance components C1, C2. The luminance component Lu is selected from 4 different luminance candidates by comparing their R,G,B coefficients and color sensor properties. For the two chrominance components C1, C2, the directions of their transform vectors are determined by the discriminant analysis and the covariance analysis in the chrominance subspace of the RGB color space. The magnitudes of their transform vectors are determined by the discriminant values of Lu, C1, C2. Extensive experiments are conducted on 4 benchmark databases to evaluate our proposed color space LuC1C2. The experimental results obtained by using 2 different color features and 3 different dimension reduction methods show that our proposed color space LuC1C2 achieves consistently better face recognition performance than state-of-the-art color spaces on 3 databases. We also show that the proposed color space achieves higher face verification rate than the state of the arts on FRGC database. Furthermore, the face verification performance is improved significantly by combining CNN features with simple raw-pixel features from the proposed LuC1C2 color space on both LFW and FRGC databases.