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

## TL;DR

This paper introduces a probabilistic framework for 2DCCA, called P2DCCA, using an EM algorithm, which improves face recognition performance under various challenging conditions.

## Contribution

It presents the first probabilistic formulation of 2DCCA and develops an EM-based algorithm for better parameter estimation in face recognition.

## Key findings

- Superior loading factor estimation over 2DCCA
- Robust face recognition across different conditions
- Effective on synthetic and real datasets

## 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.