NDLI: Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature

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Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature

Content Provider	ACM Digital Library
Author	Ward, Robert D. Ingleby, Michael Khan, Masood Mehmood
Copyright Year	2009
Abstract	Earlier researchers were able to extract the transient facial thermal features from thermal infrared images (TIRIs) to make binary distinctions between the expressions of affective states. However, effective human-computer interaction would require machines to distinguish between the subtle facial expressions of affective states. This work, for the first time, attempts to use the transient facial thermal features for recognizing a much wider range of facial expressions. A database of 324 time-sequential, visible-spectrum, and thermal facial images was developed representing different facial expressions from 23 participants in different situations. A novel facial thermal feature extraction, selection, and classification approach was developed and invoked on various Gaussian mixture models constructed using: neutral and pretended happy and sad faces, faces with multiple positive and negative facial expressions, faces with neutral and six (pretended) basic facial expressions, and faces with evoked happiness, sadness, disgust, and anger. This work demonstrates that (1) infrared imaging can be used to observe the affective-state-specific facial thermal variations, (2) pixel-grey level analysis of TIRIs can help localise significant facial thermal feature points along the major facial muscles, and (3) cluster-analytic classification of transient thermal features can help distinguish between the facial expressions of affective states in an optimized eigenspace of input thermal feature vectors. The observed classification results exhibited influence of a Gaussian mixture model's structure on classifier-performance. The work also unveiled some pertinent aspects of future research on the use of facial thermal features in automated facial expression classification and affect recognition.
Starting Page	1
Ending Page	22
Page Count	22
File Format	PDF
ISSN	15443558
e-ISSN	15443965
DOI	10.1145/1462055.1462061
Volume Number	6
Issue Number	1
Journal	ACM Transactions on Applied Perception (TAP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-02-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Physiology-based automated affect recognition Affective computing and thermal infrared imaging Facial expression classification
Content Type	Text
Resource Type	Article
Subject	Experimental and Cognitive Psychology Theoretical Computer Science Computer Science

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