Form as a Cue in the Automatic Recognition of Non-acted Affective Body Expressions
Identifieur interne : 000356 ( Main/Exploration ); précédent : 000355; suivant : 000357Form as a Cue in the Automatic Recognition of Non-acted Affective Body Expressions
Auteurs : Andrea Kleinsmith [Royaume-Uni] ; Nadia Bianchi-Berthouze [Royaume-Uni]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: The advent of whole-body interactive technology has increased the importance of creating systems that take into account body expressions to determine the affective state of the user. In doing so, the role played by the form and motion information needs to be understood. Neuroscience studies have shown that biological motion is recognized by separate pathways in the brain. This paper investigates the contribution of body configuration (form) in the automatic recognition of non-acted affective dynamic expressions in a video game context. Sequences of static postures are automatically extracted from motion capture data and presented to the system which is a combination of an affective posture recognition module and a sequence classification rule to finalize the affective state of each sequence. Our results show that using form information only, the system recognition reaches performances very close to the agreement between observers who viewed the affective expressions as animations containing both form and temporal information.
Url:
DOI: 10.1007/978-3-642-24600-5_19
Affiliations:
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In doing so, the role played by the form and motion information needs to be understood. Neuroscience studies have shown that biological motion is recognized by separate pathways in the brain. This paper investigates the contribution of body configuration (form) in the automatic recognition of non-acted affective dynamic expressions in a video game context. Sequences of static postures are automatically extracted from motion capture data and presented to the system which is a combination of an affective posture recognition module and a sequence classification rule to finalize the affective state of each sequence. Our results show that using form information only, the system recognition reaches performances very close to the agreement between observers who viewed the affective expressions as animations containing both form and temporal information.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Grand Londres</li></region><settlement><li>Londres</li></settlement><orgName><li>Université de Londres</li></orgName></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Kleinsmith, Andrea" sort="Kleinsmith, Andrea" uniqKey="Kleinsmith A" first="Andrea" last="Kleinsmith">Andrea Kleinsmith</name></region><name sortKey="Bianchi Berthouze, Nadia" sort="Bianchi Berthouze, Nadia" uniqKey="Bianchi Berthouze N" first="Nadia" last="Bianchi-Berthouze">Nadia Bianchi-Berthouze</name><name sortKey="Bianchi Berthouze, Nadia" sort="Bianchi Berthouze, Nadia" uniqKey="Bianchi Berthouze N" first="Nadia" last="Bianchi-Berthouze">Nadia Bianchi-Berthouze</name><name sortKey="Kleinsmith, Andrea" sort="Kleinsmith, Andrea" uniqKey="Kleinsmith A" first="Andrea" last="Kleinsmith">Andrea Kleinsmith</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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