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 language | English |
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Title of host publication | Proceedings of the Rapid Screening Technologies, Deception Detection and Credibility Assessment Symposium |
Subtitle of host publication | January 2013 |
Editors | Matthew Jensen, Thomas Meservy, Judee Burgoon, Jay Nunamaker |
Number of pages | 5 |
Publication status | Published - 7 Jan 2013 |
Event | HICSS-46 Rapid Screening Technologies, Deception Detection and Credibility Assessment Symposium 2013 - Maui, United States Duration: 7 Jan 2013 → 8 Jan 2013 |
Conference
Conference | HICSS-46 Rapid Screening Technologies, Deception Detection and Credibility Assessment Symposium 2013 |
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Country/Territory | United States |
City | Maui |
Period | 7/01/13 → 8/01/13 |