Color Effect on the Face Recognition with Spatial Resolution Constraints
In the practical face recognition (FR) applications, low-resolution faces (20 times 20 pixels or less) are commonly encountered and negatively impact on reliable performance. To overcome low-resolution face problem, we show that face color can significantly improve the performance compared to intensity-based features. The contribution of this paper is twofold. First, a new metric called dasiavariation ratio gainpsila (VRG) is proposed to theoretically prove the significance of color effect on low-resolution faces. Second, we conduct extensive performance comparison studies. In particular, 3,192 color facial images corresponding to 341 subjects, collected from three standard CMU PIE, FERET, and XM2VTSDB face databases, were used to perform comparative studies of color effect on various face resolutions. Experimental results verified that face color feature improves the degraded recognition rate due to low-resolution faces by at least an order of magnitude over intensity-based features.