Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature
Access Status
Authors
Date
2009Type
Metadata
Show full item recordCitation
Source Title
Additional URLs
ISSN
Faculty
Remarks
© ACM, 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the ACM Transactions on Applied Perception, {VOL. 6, ISS 1, 2009} http://doi.acm.org/10.1145/1462055.1462061
Collection
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.
Related items
Showing items related by title, author, creator and subject.
-
Benson, D.; Khan, Masood Mehmood; Tan, Tele; Hargreaves, T. (2016)© 2016 IEEE.Sociable robots can afford the ability to express emotive experiences in a realistic manner if they are able to appropriately display facial expressions and conform to the normative emotion display rules. ...
-
Khan, Masood Mehmood; Ward, R.; Ingleby, M. (2016)Automated assessment of affect and arousal level can help psychologists and psychiatrists in clinical diagnoses; and may enable affect-aware robot-human interaction. This work identifies major difficulties in automating ...
-
Khan, Masood Mehmood; Oz, Ihsan (2012)Recent works suggest that thermal intensity values (TIVs) measured around the facial thermal feature points (FTFPs) can help in distinguishing between the facial expression of affective states. This work investigates if ...