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

Jen Chun Lin, Chung Hsien Wu, Wen Li Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages3482-3486
Number of pages5
DOIs
Publication statusPublished - 2013 Oct 18
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 2013 May 262013 May 31

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period13-05-2613-05-31

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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