Color face recognition using quaternion PCA

Recently, biometric systems have attracted the attention of both academic and industrial communities. Advances in hardware and software technologies have paved the way to such growing interest. Nowadays, efficient and cost-effective biometric solutions are continuously emerging. Fingerprint-based biometric systems have emerged as pioneering commercial applications of biometric systems. Face and iris traits have proven to be reliable candidates. Until recently, face recognition research literally followed the research undertaken in the field of fingerprint recognition which is inherently gray-scale. In this paper, efforts are restricted to the investigation of face representations in the color domain. The concept of principal component analysis (PCA) is carried over into the hypercomplex domain (i.e., quaternionic) to define quaternionic PCA (Q-PCA) where color faces are compactly represented. Unlike the existing approaches for handling the color information, the proposed algorithm implicitly accounts for the correlation that exists between the face color components (i.e., red, green and blue, respectively).