Capturing Correlations Among Facial Parts for Facial Expression Analysis

Capturing and analyzing the correlations among facial parts are important for interpreting facial behaviors precisely. In this paper, we exploit Canonical Correlation Analysis (CCA) to model the correlations of facial parts for facial expression analysis. We propose a Matrix-based Canonical Correlation Analysis (MCCA) for better correlation analysis on 2D image or matrix data in general. Extensive experiments have shown that compared to the traditional CCA, MCCA models more accurately correlations among image data with more compact representation using much fewer canonical factors.