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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


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
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


Conference7th International Conference on Methods and Techniques in Behavioral Research, MB'10


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.


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