An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition
This work provides a probabilistic extension to 2DCCA for improved face recognition, addressing a specific methodological gap in image feature extraction.
The authors tackled the lack of a probabilistic interpretation in two-dimensional canonical correlation analysis (2DCCA) by proposing a probabilistic framework called P2DCCA with an EM-based algorithm, achieving superior performance in loading factor estimation and robustness in face recognition tasks across various conditions.
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.