Abstract: Face recognition is an active research area in the artificial intelligence, which has
aroused great concern. Multiple color space canonical correlation analysis is proposed based on
different color spaces analysis. This paper analyses and discusses the characteristics of Contourlet
transform, and, by using the contourlet’s advantage of multiscale, directionality and anisotropy,
proposes a novel color face recognition algorithm. First, the source images are transformed into
contourlet domain to get the LP and HP images. Then, canonical correlation analysis (CCA) is
used to recognize the face. CCA is an efficient projection operator, which can analyze different
color spaces to get the biggest correlation. Finally, nearest neighbor classifier is selected to
perform face recognition. Experimental results on color AR face database show that the proposed
algorithm, which achieves recognition accuracy of above 98%, is more effective and faster than
the traditional method.

Key words: color face recognition, contourlet, canonical correlation analysis (CCA), nearest
neighbor classifier