Automatic inference of mental states from spontaneous facial expressions

Yanjia Sun, Ali N. Akansu

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

5 Scopus citations

Abstract

Human face is a display of mental states that reflect the true feelings of a person. In this paper, we propose a framework for the video analysis of spontaneous facial expressions using an automatic facial emotion recognition system. Regional Hidden Markov Models (RHMMs) are created to describe the states of facial attributes for eyebrows, eyes, and mouth regions registered in a video sequence. The performance results reported in the paper show that the proposed technique outperforms the designated HMM for each emotion type [1, 2] tested with the Cohn-Kanade database for the person-independent case. More importantly, we used the proposed system to infer the mental states of a person based on spontaneous facial expressions. Merit of the proposed system is validated with human based evaluations.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages719-723
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

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

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Automatic facial emotion recognition
  • Regional Hidden Markov Model
  • facial perception
  • mental states
  • states of face regions

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