This paper introduces the new color face recognition (FR) method that makes effective use of boosting
learning as color-component feature selection framework. The proposed boosting color-component
feature selection framework is designed for finding the best set of color-component features from various
color spaces (or models), aiming to achieve the best FR performance for a given FR task. In addition, to
facilitate the complementary effect of the selected color-component features for the purpose of color FR,
they are combined using the proposed weighted feature fusion scheme. The effectiveness of our color FR
method has been successfully evaluated on the following five public face databases (DBs): CMU-PIE,
Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that the results of the
proposed method are impressively better than the results of other state-of-the-art color FR methods over
different FR challenges including highly uncontrolled illumination, moderate pose variation, and small
resolution face images.
Issue Date
2011-07-14
Keywords
Color face recognition; boosting learning; color space; color-component; feature selection; weighted feature fusion