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Facial expression classification based on local spatiotemporal edge and texture descriptors

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationSelected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10
DOIs
Publication statusPublished - 2011
Publication typeA4 Article in a conference publication
Event7th International Conference on Methods and Techniques in Behavioral Research, MB'10 - Eindhoven, Netherlands
Duration: 24 Aug 201027 Aug 2010

Conference

Conference7th International Conference on Methods and Techniques in Behavioral Research, MB'10
CountryNetherlands
CityEindhoven
Period24/08/1027/08/10

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.

Keywords

  • Action unit, Emotion, Expression classification, Facial expression, Human behaviour understanding, Local binary pattern, Local oriented edge, Spatiotemporal descriptor