<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Leo, Marco</dc:creator>
  <dc:creator>Carcagnì, Pierluigi</dc:creator>
  <dc:creator>Distante, Cosimo</dc:creator>
  <dc:creator>Spagnolo, Paolo</dc:creator>
  <dc:creator>Mazzeo, Pier Luigi</dc:creator>
  <dc:creator>Rosato, Anna Chiara</dc:creator>
  <dc:creator>Petrocchi, Serena</dc:creator>
  <dc:creator>Pellegrino, Chiara</dc:creator>
  <dc:creator>Levante, Annalisa</dc:creator>
  <dc:creator>De Lumè, Filomena</dc:creator>
  <dc:creator>Lecciso, Flavia</dc:creator>
  <dc:date>2018-11-16</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">In this paper, a computational approach is proposed and put into practice to  assess the capability of children having had diagnosed Autism Spectrum  Disorders (ASD) to produce facial expressions. The proposed approach is  based on computer vision components working on sequence of images acquired  by an off-the-shelf camera in unconstrained conditions. Action unit intensities  are estimated by analyzing local appearance and then both temporal and  geometrical relationships, learned by Convolutional Neural Networks, are  exploited to regularize gathered estimates. To cope with stereotyped movements  and to highlight even subtle voluntary movements of facial muscles, a  personalized and contextual statistical modeling of non-emotional face is  formulated and used as a reference. Experimental results demonstrate how the  proposed pipeline can improve the analysis of facial expressions produced by  ASD children. A comparison of system’s outputs with the evaluations performed  by psychologists, on the same group of ASD children, makes evident how the  performed quantitative analysis of children’s abilities helps to go beyond the  traditional qualitative ASD assessment/diagnosis protocols, whose outcomes  are affected by human limitations in observing and understanding multi-cues  behaviors such as facial expressions.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://n2t.net/ark:/12658/srd1318990</dc:identifier>
  <dc:identifier>https://susi.usi.ch/global/documents/318990</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/318990/files/Leo_S_2018.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.3390/s18113993</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1318990</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>CC BY</dc:rights>
  <dc:source>Sensors. - 2018, vol. 18, no. 11, p. 3993</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Quantitative facial expression analysis</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Geometrical and temporal regularization of facial action units</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">ASD diagnosis and assessment</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/61</dc:subject>
  <dc:title xmlns:ns4="xml" ns4:lang="en">Computational assessment of facial expression production in ASD children</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
