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Author: Zakia Hammal

Affiliation: Laboratory of Images and Signals, France

ISBN: 972-8865-40-6

ISSN: 2184-4321

Keyword(s): facial expression classification, dynamic modelling, transferable belief model, facial feature behavior.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: In the present contribution a novel approach for dynamic facial expressions classification is presented and discussed. The presented approach is based on the use of the Transferable Belief Model applied to static facial expression classification studied in previous developments. The system is able to recognize pure facial expressions, i.e., Joy, Surprise, Disgust and Neutral as well as their mixtures. Additionally, this approach is able to deal with all facial feature configurations that does not correspond to any of the cited expression, i.e., Unknown expressions. The major improvement of this former work consists in the introduction of the temporal evolution of the facial feature behavior. Initially, the temporal information is introduced to improve the robustness of the frame-by-frame classification by the correction of errors due to the automatic segmentation process. In addition since a facial expression is the result of a dynamic and progressive combination of facial features be havior, which is not always synchronous, a frame-by-frame classification is not sufficient. To overcome this constraint, we propose the introduction of the temporal information inside the TBM fusion framework. The recognition is accomplished by combining all facial feature behaviors between the beginning and the end of an expression sequence independently to their chronological order. Then the final decision is taken on the whole sequence and consequently, the recognition becomes more robust and accurate. Experimental results on the Hammal Caplier database demonstrate the improvement on the frame-by-frame classification and the ability to recognize entire facial expression sequences. (More)

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Paper citation in several formats:
Hammal, Z. (2006). DYNAMIC FACIAL EXPRESSION UNDERSTANDING BASED ON TEMPORAL MODELLING OF TRANSFERABLE BELIEF MODEL.In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 972-8865-40-6, pages 93-100. DOI: 10.5220/0001377600930100

@conference{visapp06,
author={Zakia Hammal},
title={DYNAMIC FACIAL EXPRESSION UNDERSTANDING BASED ON TEMPORAL MODELLING OF TRANSFERABLE BELIEF MODEL},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2006},
pages={93-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001377600930100},
isbn={972-8865-40-6},
}

TY - CONF

JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - DYNAMIC FACIAL EXPRESSION UNDERSTANDING BASED ON TEMPORAL MODELLING OF TRANSFERABLE BELIEF MODEL
SN - 972-8865-40-6
AU - Hammal, Z.
PY - 2006
SP - 93
EP - 100
DO - 10.5220/0001377600930100

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