Quaternion Decomposition Based Discriminant Analysis for Color Face Recognition

In this paper, we propose a novel quaternion decomposition based discriminant analysis (QDDA) method for color face recognition. Unlike traditional approaches that handle color face images by vector representation or by each color channel individually, QDDA makes use of the quaternion to encode all color channels such that we can process all these channels in a holistic way and consider their relations simultaneously. In order to extract more discriminant color information from the image, a decomposition operation is performed to the quaternion matrix. A linear discriminant analysis is finally implemented to the obtained subcomponents for feature extraction. Experimental results have demonstrated the effectiveness of QDDA by comparing with other quaternion based methods.