Automated human behavioral analysis framework using facial feature extraction and machine learning

Demiyan Smirnov, Sean Banger, Sara Davis, Rajani Muraleedharan, Ravi Ramachandran

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

1 Scopus citations

Abstract

Emotional intelligence is essential in understanding and predicting human behavior. Although human emotion is best captured using non-intrusive methods, due to factors such as system complexity, computation time and decision response time, the reality of automated behavioral analysis is hindered. In this paper, we propose a framework capable of recognizing emotions of an individual to identify any suspicious behavior. Our research shows 91.1% of emotion classification accuracy for cooperative individuals using facial feature extraction and machine learning techniques, thus outperforming existing state-of-the-art approaches.

Original languageEnglish (US)
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages911-914
Number of pages4
ISBN (Print)9781479923908
DOIs
StatePublished - Jan 1 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Smirnov, D., Banger, S., Davis, S., Muraleedharan, R., & Ramachandran, R. (2013). Automated human behavioral analysis framework using facial feature extraction and machine learning. In Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers (pp. 911-914). [6810420] (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810420