A Color Channel Fusion Approach for Face Recognition

Due to high dimensionality of images or generated color features, different color channels are usually processed separately and then concatenated together into a feature vector for classification. This makes channel fusion a crucial step in color face recognition (FR) systems. However, existing methods simply concatenate channel-wise color features without identifying the importance or reliability of features in different color channels. In this paper, we propose a color channel fusion (CCF) approach using jointly dimension reduction algorithms to select more features from reliable and discriminative channels. Experiments using two different dimension reduction approaches, two different types of features on three image datasets show that CCF achieves consistently better performance than color channel concatenation (CCC) method which deals with different color channels equally.