Extracting multiple features in the CID color space for face recognition

Abstract

This correspondence presents a novel face recognition method that extracts multiple features in the color image discriminant (CID) color space, where three new color component images, D1, D2, and D3, are derived using an iterative algorithm. As different color component images in the CID color space display different characteristics, three different image encoding methods are presented to effectively extract features from the component images for enhancing pattern recognition performance. To further improve classification performance, the similarity scores due to the three color component images are fused for the final decision making. Experimental results using two large-scale face databases, namely, the face recognition grand challenge (FRGC) version 2 database and the FERET database, show the effectiveness of the proposed method.