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Automated facial expression classification and affect interpretation using infrared measurement of facial skin temperature

Khan, Masood Mehmood, Ingleby, Michael and Ward, Robert D. (2006) Automated facial expression classification and affect interpretation using infrared measurement of facial skin temperature. ACM Transactions on Autonomous and Adaptive Systems, 1 (1). pp. 91-113. ISSN 1556-4665

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    Abstract

    Machines would require the ability to perceive and adapt to affects for achieving artificial sociability.
    Most autonomous systems use Automated Facial Expression Classification (AFEC) and Automated
    Affect Interpretation (AAI) to achieve sociability. Varying lighting conditions, occlusion, and control
    over physiognomy can influence the real life performance of vision-based AFEC systems. Physiological
    signals provide complementary information for AFEC and AAI. We employed transient
    facial thermal features for AFEC and AAI. Infrared thermal images with participants’ normal
    expression and intentional expressions of happiness, sadness, disgust, and fear were captured. Facial
    points that undergo significant thermal changes with a change in expression termed as Facial
    Thermal Feature Points (FTFPs) were identified. Discriminant analysis was invoked on principal
    components derived from the Thermal Intensity Values (TIVs) recorded at the FTFPs. The crossvalidation
    and person-independent classification respectively resulted in 66.28% and 56.0% success
    rates. Classification significance tests suggest that (1) like other physiological cues, facial skin
    temperature also provides useful information about affective states and their facial expression; (2)
    patterns of facial skin temperature variation can complement other cues for AFEC and AAI; and (3)
    infrared thermal imaging may help achieve artificial sociability in robots and autonomous systems.

    Item Type: Article
    Additional Information: UoA 23 (Computer Science and Informatics) © ACM Press New York, NY, USA
    Uncontrolled Keywords: socially intelligent machines, infrared thermal imaging
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QP Physiology
    Schools: School of Computing and Engineering
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    Depositing User: Sara Taylor
    Date Deposited: 12 Jul 2007
    Last Modified: 28 Jul 2010 19:20
    URI: http://eprints.hud.ac.uk/id/eprint/288

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