A comparison of facial features and fusion methods for emotion recognition

Demiyan V. Smirnov, Rajani Muraleedharan, Ravi Ramachandran

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

1 Scopus citations

Abstract

Emotion recognition is an important part of human behavior analysis. It finds many applications including human-computer interaction, driver safety, health care, stress detection, psychological analysis, forensics, law enforcement and customer care. The focus of this paper is to use a pattern recognition framework based on facial expression features and two classifiers (linear discriminant analysis and k-nearest neighbor) for emotion recognition. The extended Cohn-Kanade data- base is used to classify 5 emotions, namely, ‘neutral, angry, disgust, happy, and surprise’. The Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), the Walsh-Hadamard Transform (FWHT) and a new 7-dimensional feature based on condensing the Facial Action Coding System (FACS) are compared. Ensemble systems using decision level, score fusion and Borda count are also studied. Fusion of the four features leads to slightly more than a 90% accuracy.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
PublisherSpringer Verlag
Pages574-582
Number of pages9
ISBN (Print)9783319265605
DOIs
StatePublished - Jan 1 2015
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: Nov 9 2015Nov 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9492
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Neural Information Processing, ICONIP 2015
CountryTurkey
CityIstanbul
Period11/9/1511/12/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Smirnov, D. V., Muraleedharan, R., & Ramachandran, R. (2015). A comparison of facial features and fusion methods for emotion recognition. In S. Arik, T. Huang, W. K. Lai, & Q. Liu (Eds.), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings (pp. 574-582). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492). Springer Verlag. https://doi.org/10.1007/978-3-319-26561-2_68