Improving the Face Recognition Grand Challenge Baseline Performance using Color Configurations Across Color Spaces

This paper presents a method that applies color information to improve face recognition performance of the face recognition grand challenge (FRGC) baseline algorithm, also known as the biometric experimentation environment (BEE) baseline algorithm. In particular, we empirically assess the face recognition performance of the BEE baseline algorithm by applying color configurations in the YIQ and the YC_bC_r color spaces. The color configuration is defined as an individual or a combination of color component images. Experimental results using an FRGC ver1.0 dateset containing 1,126 images demonstrate that the YQCr color configuration improves the rank-one face recognition rate of the BEE baseline algorithm from 37% to 70%; when experimenting with an FRGC ver2.0 dataset consisting of 30,702 images, the YQCr color configuration achieves 65% verification rate comparing to the FRGC baseline performance of 12%.