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A computational study of expressive facial dynamics in children with autism

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Guha, Tanaya, Yang, Zhaojun, Grossman, Ruth B. and Narayanan, Shrikanth S. (2018) A computational study of expressive facial dynamics in children with autism. IEEE Transactions on Affective Computing, 9 (1). pp. 14-20. doi:10.1109/TAFFC.2016.2578316 ISSN 1939-1374.

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Official URL: http://dx.doi.org/10.1109/TAFFC.2016.2578316

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Abstract

Several studies have established that facial expressions of children with autism are often perceived as atypical, awkward or less engaging by typical adult observers. Despite this clear deficit in the quality of facial expression production, very little is understood about its underlying mechanisms and characteristics. This paper takes a computational approach to studying details of facial expressions of children with high functioning autism (HFA). The objective is to uncover those characteristics of facial expressions, notably distinct from those in typically developing children, and which are otherwise difficult to detect by visual inspection. We use motion capture data obtained from subjects with HFA and typically developing subjects while they produced various facial expressions. This data is analyzed to investigate how the overall and local facial dynamics of children with HFA differ from their typically developing peers. Our major observations include reduced complexity in the dynamic facial behavior of the HFA group arising primarily from the eye region.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Human face recognition (Computer science), Face perception, Autistic children, Facial expression, Computer vision
Journal or Publication Title: IEEE Transactions on Affective Computing
Publisher: IEEE
ISSN: 1939-1374
Official Date: 7 March 2018
Dates:
DateEvent
7 March 2018Published
7 June 2016Available
5 June 2016Accepted
2 March 2016Submitted
Volume: 9
Number: 1
Page Range: pp. 14-20
DOI: 10.1109/TAFFC.2016.2578316
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 11 October 2018
Date of first compliant Open Access: 12 October 2018
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDNational Science Foundationhttp://dx.doi.org/10.13039/100000001
UNSPECIFIEDNational Institutes of Healthhttp://dx.doi.org/10.13039/100000002

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