An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition

Noname manuscript No. (will be inserted by the editor) 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. Keywords Canonical Correlation Analysis (CCA) · Two-dimensional CCA · Probabilistic Feature extraction · Dimension Reduction · Face recognition 1 Introduction Although many real-world applications encounter high dimensional data, the most informative part of the data can be modeled in a low dimensional space. Moreover, processing high-dimensional data is a time consuming process and requires lots of resources. To tackle these problems, feature extraction has been used as a tool for finding a compact and meaningful data representation. For single-mode source data, some subspace learning methods are conducted to learn more semantic description subspaces.