Sparse temporal representations for facial expression recognition

Chew, Sien Wei, Rana, Rajib, Lucey, Patrick J., Lucey, Simon, & Sridharan, Sridha (2011) Sparse temporal representations for facial expression recognition. In Lecture Notes in Computer Science, Springer-Verlag, Gwangju, South Korea.

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In automatic facial expression recognition, an increasing number of techniques had been proposed for in the literature that exploits the temporal nature of facial expressions. As all facial expressions are known to evolve over time, it is crucially important for a classifier to be capable of modelling their dynamics. We establish that the method of sparse representation (SR) classifiers proves to be a suitable candidate for this purpose, and subsequently propose a framework for expression dynamics to be efficiently incorporated into its current formulation. We additionally show that for the SR method to be applied effectively, then a certain threshold on image dimensionality must be enforced (unlike in facial recognition problems). Thirdly, we determined that recognition rates may be significantly influenced by the size of the projection matrix \Phi. To demonstrate these, a battery of experiments had been conducted on the CK+ dataset for the recognition of the seven prototypic expressions - anger, contempt, disgust, fear, happiness, sadness and surprise - and comparisons have been made between the proposed temporal-SR against the static-SR framework and state-of-the-art support vector machine.

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17 citations in Scopus
2 citations in Web of Science®
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ID Code: 46184
Item Type: Conference Paper
Refereed: Yes
Additional Information: Published version attached and locked until authors send a n accepted version
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Keywords: automatic facial expression recognition, sparse representation classi�cation, temporal framework
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2011 Springer Verlag
Deposited On: 27 Sep 2011 22:37
Last Modified: 14 Jul 2017 11:01

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