Sparse Representation Feature for Facial Expression Recognition

被引：2

作者：

Yue, Caitong ^{[1
]}

Liang, Jing ^{[1
]}

Qu, Boyang ^{[2
]}

Lu, Zhuopei ^{[1
]}

Li, Baolei ^{[3
]}

Han, Yuhong ^{[4
,5
]}

机构：

[1] Zhengzhou Univ, Sch Elect Engn, Zhengzhou, Henan, Peoples R China

[2] Zhongyuan Univ Technol, Sch Elect & Informat Engn, Zhengzhou, Henan, Peoples R China

[3] Nanyang Normal Univ, Phys & Elect Engn Coll, Nanyang, Peoples R China

[4] South China Univ Technol, MOE Key Lab Specially Funct Mat, Guangzhou, Peoples R China

[5] South China Univ Technol, Inst Opt Commun Mat, Guangzhou, Peoples R China

来源：

PROCEEDINGS OF ELM-2017 | 2019年 / 10卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Facial expression recognition; Sparse representation; Extreme learning machine; TELM;

D O I：

10.1007/978-3-030-01520-6_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Facial expression recognition is a challenging task, because it is difficult to recognize facial expressions of different persons if they are of diverse races and ages. Extracting distinctive feature from original facial image is a critical step for successful facial expression recognition. This paper proposes sparse representation feature for facial expression recognition. First of all, a dictionary is established using training images. Then sparse representation feature is extracted by sparse representation orthogonal matching pursuit method. Finally the extracted features of different expressions are classified by two-hidden-layer extreme learning machine. Facial expression images of both Cohn-Kanade and JAFFE databases are classified using sparse representation feature. Experimental results show that the sparse representation feature is suitable for facial expression recognition.

引用

页码：12 / 21

页数：10

共 17 条

[1]

[Anonymous], 2005, HDB FACE RECOGNITION

[2] CONSTANTS ACROSS CULTURES IN FACE AND EMOTION [J].

EKMAN, P ;

FRIESEN, WV .

JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY, 1971, 17 (02) :124-&

[3] Wavelet feature selection for image classification [J].

Huang, Ke ;

Aviyente, Selin .

IEEE TRANSACTIONS ON IMAGE PROCESSING, 2008, 17 (09) :1709-1720

[4] Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification [J].

Jia, Sen ;

Shen, Linlin ;

Li, Qingquan .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (02) :1118-1129

[5]

Jiang B, 2008, PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P407, DOI 10.1109/ICMLC.2008.4620440

[6]

Kharat G. U., 2008, WSEAS Transactions on Computers, V7, P650

[7] PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data [J].

Kuncheva, Ludmila I. ;

Faithfull, William J. .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (01) :69-80

[8]

Long J, 2015, PROC CVPR IEEE, P3431, DOI 10.1109/CVPR.2015.7298965

[9] Facial Expression Recognition Using Stationary Wavelet Transform Features [J].

Qayyum, Huma ;

Majid, Muhammad ;

Anwar, Syed Muhammad ;

Khan, Bilal .

MATHEMATICAL PROBLEMS IN ENGINEERING, 2017, 2017

[10] Two-hidden-layer extreme learning machine for regression and classification [J].

Qu, B. Y. ;

Lang, B. F. ;

Liang, J. J. ;

Qin, A. K. ;

Crisalle, O. D. .

NEUROCOMPUTING, 2016, 175 :826-834

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