Title: An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition,
Abstract: Recently, two-dimensional canonical correlation analysis (2DCCA) has been
successfully applied for image feature extraction. The method instead of
concatenating the columns of the images to the one-dimensional vectors,
directly works with two-dimensional image matrices. Although 2DCCA works well
in different recognition tasks, it lacks a probabilistic interpretation. In
this paper, we present a probabilistic framework for 2DCCA called probabilistic
2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the
parameters. Experimental results on synthetic and real data demonstrate
superior performance in loading factor estimation for P2DCCA compared to 2DCCA.
For real data, three subsets of AR face database and also the UMIST face
database confirm the robustness of the proposed algorithm in face recognition
tasks with different illumination conditions, facial expressions, poses and
occlusions.