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Feature Disentangling Machine - A Novel Approach of Feature Selection and Disentangling in Facial Expression Analysis*
Ping Liu1, Joey Tianyi Zhou2, Ivor Wai-Hung Tsang3, Zibo Meng1, Shizhong Han1, and Yan Tong1
1Department of Computer Science, University of South Carolina, USA
2Center for Computational Intelligence, Nanyang Technology University, Singapore
3Center for Quantum Computation and Intelligent Systems, University of Technology, Australia
Abstract. Studies in psychology show that not all facial regions are of importance in recognizing facial expressions and different facial regions make different contributions in various facial expressions. Motivated by this, a novel framework, named Feature Disentangling Machine (FDM), is proposed to effectively select active features characterizing facial expressions. More importantly, the FDM aims to disentangle these selected features into non-overlapped groups, in particular, common features that are shared across different expressions and expression-specific features that are discriminative only for a target expression. Specifically, the FDM integrates sparse support vector machine and multi-task learning in a unified framework, where a novel loss function and a set of constraints are formulated to precisely control the sparsity and naturally disentangle active features. Extensive experiments on two well-known facial expression databases have demonstrated that the FDM outperforms the state-of-the-art methods for facial expression analysis. More importantly, the FDM achieves an impressive performance in a cross-database validation, which demonstrates the generalization capability of the selected features.
Electronic Supplementary Material:
LNCS 8692, p. 151 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014