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26749 Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation
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Kaltwang, S. and Todorovic, S. and Pantic, M. (2016) Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation. IEEE transactions on pattern analysis and machine intelligence, 38 (9). pp. 1748-1761. ISSN 0162-8828 *** ISI Impact 6,077 ***

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Certain inner feelings and physiological states like pain are subjective states that cannot be directly measured, but can be estimated from spontaneous facial expressions. Since they are typically characterized by subtle movements of facial parts, analysis of the facial details is required. To this end, we formulate a new regression method for continuous estimation of the intensity of facial behavior interpretation, called Doubly Sparse Relevance Vector Machine (DSRVM). DSRVM enforces double sparsity by jointly selecting the most relevant training examples (a.k.a. relevance vectors) and the most important kernels associated with facial parts relevant for interpretation of observed facial expressions. This advances prior work on multi-kernel learning, where sparsity of relevant kernels is typically ignored. Empirical evaluation on challenging Shoulder Pain videos, and the benchmark DISFA and SEMAINE datasets demonstrate that DSRVM outperforms competing approaches with a multi-fold reduction of running times in training and testing.

Item Type:Article
Research Group:EWI-HMI: Human Media Interaction
Research Program:CTIT-General
Uncontrolled Keywords:Regression, Relevance Vector Machine, Multiple Kernel Learning, Facial expressions
ID Code:26749
Deposited On:08 February 2016
ISI Impact Factor:6,077
More Information:statisticsmetis

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