Paper Info
Title
Feature Disentangling Machine - A Novel Approach Of Feature Selection And Disentangling In Facial Expression Analysis
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.
Year
DOI
Venue
2014
10.1007/978-3-319-10593-2_11
COMPUTER VISION - ECCV 2014, PT IV
Field
DocType
Volume
Expression (mathematics),Facial expression recognition,Pattern recognition,Feature selection,Computer science,Support vector machine,Local binary patterns,Facial expression,Artificial intelligence,Discriminative model
Conference
8692
ISSN
Citations 
PageRank 
0302-9743
18
0.63
References 
Authors
30
6
Name
Order
Citations
PageRank
Ping Liu135916.70
Joey Tianyi Zhou235438.60
Ivor W. Tsang35396248.44
Zibo Meng424813.60
Shizhong Han52449.80
Yan Tong62449.93