Fusion of feature sets and classifiers for facial expression recognition

被引：107

作者：

Zavaschi, Thiago H. H. ^{[1
]}

Britto, Alceu S., Jr. ^{[1
]}

Oliveira, Luiz E. S. ^{[2
]}

Koerich, Alessandro L. ^{[1
,2
]}

机构：

[1] Pontif Catholic Univ Parana PUCPR, BR-80215901 Curitiba, PR, Brazil

[2] Univ Fed Parana, BR-81531990 Curitiba, Parana, Brazil

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2013年 / 40卷 / 02期

关键词：

Face recognition; Emotion recognition; Ensemble of classifiers; Feature selection; FACE; CLASSIFICATION; EMOTION;

D O I：

10.1016/j.eswa.2012.07.074

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a novel method for facial expression recognition that employs the combination of two different feature sets in an ensemble approach. A pool of base support vector machine classifiers is created using Gabor filters and Local Binary Patterns. Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the minimization of both the error rate and the size of the ensemble. Experimental results on JAFFE and Cohn-Kanade databases have shown the efficiency of the proposed strategy in finding powerful ensembles, which improves the recognition rates between 5% and 10% over conventional approaches that employ single feature sets and single classifiers. (c) 2012 Elsevier Ltd. All rights reserved.

引用

页码：646 / 655

页数：10

共 43 条

[1] Automatic facial expression recognition using facial animation parameters and multistream HMMs [J].

Aleksic, Petar S. ;

Katsaggelos, Aggelos K. .

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2006, 1 (01) :3-11

[2]

[Anonymous], 2004, COMBINING PATTERN CL, DOI DOI 10.1002/0471660264

[3]

Bartlett MS, 2005, PROC CVPR IEEE, P568

[4] Recognition of facial expressions using Gabor wavelets and learning vector quantization [J].

Bashyal, Shishir ;

Venayagamoorthy, Ganesh K. .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2008, 21 (07) :1056-1064

[5] Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection [J].

Belhumeur, PN ;

Hespanha, JP ;

Kriegman, DJ .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19 (07) :711-720

[6] Optical flow based analyses to detect emotion from human facial image data [J].

Besinger, Axel ;

Sztynda, Tamara ;

Lal, Sara ;

Duthoit, Carmen ;

Agbinya, Johnson ;

Jap, Budi ;

Eager, David ;

Dissanayake, Gamini .

EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (12) :8897-8902

[7]

Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1023/A:1018054314350

[8]

Cantu-Paz E., 2000, EFFICIENT ACCURATE P

[9] Facial Expression Recognition in JAFFE Dataset Based on Gaussian Process Classification [J].

Cheng, Fei ;

Yu, Jiangsheng ;

Xiong, Huilin .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (10) :1685-1690

[10] Facial expression recognition from video sequences: temporal and static modeling [J].

Cohen, I ;

Sebe, N ;

Garg, A ;

Chen, LS ;

Huang, TS .

COMPUTER VISION AND IMAGE UNDERSTANDING, 2003, 91 (1-2) :160-187

← 1 2 3 4 5 →