From Grayscale to Color: Quaternion Linear Regression for Color Face Recognition

Linear regression has shown an effective tool for face recognition in recent years. Most existing linear regression based methods are devised for grayscale image based face recognition and fail to exploit the color information of color face images. To extend linear regression for color images, we propose a novel color face recognition method by formulating the color face recognition problem as a quaternion linear regression model. The proposed quaternion linear regression classification (QLRC) algorithm models each color facial image as a quaternion signal and codes multiple channels of each query color image in a holistic manner. Thus, the correlation among distinct channels of each color image is well preserved and leveraged by QLRC to further improve the recognition performance. To further improve QLRC, we propose a quaternion collaborative representation optimized classifier (QCROC) which integrates QLRC and quaternion collaborative representation based classifier into a unified framework. The experiments on benchmark datasets demonstrate the efficacy of the proposed approaches for color face recognition.