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
    Country/TerritoryUnited 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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