Facial action unit prediction under partial occlusion based on Error Weighted Cross-Correlation Model

Occlusive effect is a crucial issue that may dramatically degrade performance on facial expression recognition. As emotion recognition from facial expression is based on the entire facial feature, occlusive effect remains a challenging problem to be solved. To manage this problem, an Error Weighted Cross-Correlation Model (EWCCM) is proposed to effectively predict the facial Action Unit (AU) under partial facial occlusion from non-occluded facial regions for providing the correct AU information for emotion recognition. The Gaussian Mixture Model (GMM)-based Cross-Correlation Model (CCM) in EWCCM is first proposed not only modeling the extracted facial features but also constructing the statistical dependency among features from paired facial regions for AU prediction. The Bayesian classifier weighting scheme is then adopted to explore the contributions of the GMM-based CCMs to enhance the prediction accuracy. Experiments show that a promising result of the proposed approach can be obtained.