Download Verbal and Nonverbal Features of Human-Human and by Anna Esposito, Nikolaos Bourbakis, Nikolaos Avouris, Ioannis PDF

By Anna Esposito, Nikolaos Bourbakis, Nikolaos Avouris, Ioannis Hatzilygeroudis

This publication constitutes the refereed complaints of the fee 2102 foreign convention on Verbal and Nonverbal positive factors of Human-Human and Human-Machine interplay, held in Patras, Greece, October 29 -31, 2007. The 21 revised complete papers have been conscientiously reviewed and chosen. The papers are geared up in topical sections on static and dynamic processing of faces, facial expressions and gaze in addition to emotional speech synthesis and recognition.

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Extra info for Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction: COST Action 2102 International Conference, Patras, Greece, October 29-31,

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Comprehensive database for facial expression analysis. In: Proceedings of the fourth IEEE International conference on automatic face and gesture recognition (FG 2000), Grenoble, France, pp. 46–53 (2000) 23. : Genuine, suppressed and faked facial expressions of pain in children. Pain 126(1-3), 64–71 (2006) 24. : Dynamics of facial expression extracted automatically from video. J. Image & Vision Computing 24(6), 615– 625 (2006) 25. : Cortical innervation of the facial nucleus in the non-human primate: a new interpretation of the effects of stroke and related subtotal brain trauma on the muscles of facial expression.

The analysis of a human face emotional expression requires a number of pre-processing steps which attempt to detect or track the face, to locate characteristic facial regions such as eyes, mouth and forehead on it, to extract and follow the movement of facial features, such as characteristic points in these regions, and model facial gestures using anatomic information about the face. Most of the above techniques are based on a system for describing visually distinguishable facial movements, called Facial Action Coding System (FACS) [19, 34].

SPNG Associations [37]: Here we present the results of SPN graph associations for extracted facial features (mouth) from different image frames, (see Figure 6). More specifically, when the facial features are detected and tracked in different frames, the L-G graph method is used to establish a local graph for each region. These regiongraphs are then associated to produce the global graph that connects all the regiongraphs. Then each L-G graph is associated with other L-G Graphs (extracted from the next image frames) by producing a sequence of facial expressions related with a particular emotion.

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