Probabilistic two-dimensional canonical correlation analysis for face recognition

Recently, two-dimensional canonical correlation analysis (2DCCA) proved to be an efficient technique for image feature extraction. In this paper we present a method of 2DCCA with probabilistic framework called probabilistic 2DCCA (P2DCCA), which is robust to noise and is able to cope with missing data problems. The experimental recognition results on three subsets of AR face database show the robustness of the proposed algorithm in face recognition in different illumination conditions, facial expressions and occlusion.