Facial expression classification based on local spatiotemporal edge and texture descriptors

Authors:
Yulia Gizatdinova;Veikko Surakka;Guoying Zhao;Erno Mäkinen;Roope Raisamo
Affiliations:
University of Tampere, Finland;University of Tampere, Finland;University of Oulu, Finland;University of Tampere, Finland;University of Tampere, Finland
Venue:
Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research
Year:
2010

Citing 11
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Abstract

Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims at development of a novel spatiotemporal approach to expression classification in video. The novelty comes from a new facial representation that is based on local spatiotemporal feature descriptors. In particular, a combined dynamic edge and texture information is used for reliable description of both appearance and motion of the expression. Support vector machines are utilized to perform a final expression classification. The planned experiments will further systematically evaluate the performance of the developed method with several databases of complex facial expressions.