A Hybrid System for On-line Blink Detection

Yijia Sun, Stefanos Zafeiriou, Maja Pantic

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    Abstract

    Eye blinking behaviour has been shown to be one of the most informative non-verbal behavioural cues for indicating deceptive behaviour. Traditional blink detection methods tend to use a tracker to extract static eye region images and classify those images as open and closed eyes in order to detect blinks. However, those recognition systems are frame based and do not incorporate temporal information. For this reason, they perform poorly as the tracker fails to detect eyes due to rapid head movement or occlusion. In this paper, we present an approach which combines Hidden Markov Models and Support Vector Machines to model the temporal dynamics of eye blinks and improve the blink detection accuracy.
    Original languageEnglish
    Title of host publicationProceedings of the Rapid Screening Technologies, Deception Detection and Credibility Assessment Symposium
    Subtitle of host publicationJanuary 2013
    EditorsMatthew Jensen, Thomas Meservy, Judee Burgoon, Jay Nunamaker
    Number of pages5
    Publication statusPublished - 7 Jan 2013
    EventHICSS-46 Rapid Screening Technologies, Deception Detection and Credibility Assessment Symposium 2013 - Maui, United States
    Duration: 7 Jan 20138 Jan 2013

    Conference

    ConferenceHICSS-46 Rapid Screening Technologies, Deception Detection and Credibility Assessment Symposium 2013
    CountryUnited States
    CityMaui
    Period7/01/138/01/13

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  • Cite this

    Sun, Y., Zafeiriou, S., & Pantic, M. (2013). A Hybrid System for On-line Blink Detection. In M. Jensen, T. Meservy, J. Burgoon, & J. Nunamaker (Eds.), Proceedings of the Rapid Screening Technologies, Deception Detection and Credibility Assessment Symposium: January 2013