融合局部纹理和形状特征的人脸表情识别

doi:10.11999/JEIT170799

Abstract
Figure/Table
References (19)
Related Citation (3)

Download: PDF (2452 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to improve the inadequacies of Local Binary Pattern (LBP), Center-Symmetric Local Binary Pattern (CS-LBP) and Histogram of Oriented Gradient (HOG) algorithm, Center-Symmetric Local Smooth Binary Pattern (CS-LSBP) and Histogram of Oriented Absolute Gradient (HOAG) are proposed, and a facial expression recognition method based on local texture and local shape features is proposed in this paper. Firstly, CS-LSBP and HOAG are used to extract two local features of expression image of the face. Then, Canonical Correlation Analysis (CCA) is used to fuse two local features. Finally, Support Vector Machine (SVM) is performed for the expression classification. Experimental results on JAFFE and Cohn-Kanade (CK) facial expression databases show that, the improved feature extraction method can extract the detail information of the image more completely and accurately. And the fusion method based on CCA can give full play to the representation ability of each feature. The facial expression recognition method proposed in this paper obtains a better recognition effect.

Key words： Facial expression recognition Center-Symmetric Local Smooth Binary Pattern (CS-LSBP) Histogram of Oriented Absolute Gradient (HOAG) Canonical Correlation Analysis (CCA)

Received: 07 August 2017 Published: 14 March 2018

PACS:

TP391.4

Fund:The National Natural Science Foundation of China (61672202, 61432004, 61502141), The National Natural Science Foundation of China-Shenzhen Joint Foundation (Key Project) (U1613217), The Key University Science Research Project of Anhui Province (KJ2017A368)

Corresponding Authors: HU Min E-mail: uhnim@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	HU Min
	TENG Wendi
	WANG Xiaohua
	XU Liangfeng
	YANG Juan

Cite this article:

HU Min,TENG Wendi,WANG Xiaohua等. Facial Expression Recognition Based on Local Texture and Shape Features[J]. JEIT, 2018, 40(6): 1338-1344.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT170799 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I6/1338

[1]	KYPEROUNTAS M, TEFAS A, and PITAS I. Salient feature and reliable classifier selection for facial expression classification[J]. Pattern Recognition, 2010, 43(3): 972-986. doi: 10.1016/j.patcog.2009.07.007.
[2]	付晓峰, 韦巍. 基于多尺度中心化二值模式的人脸表情识别[J]. 控制理论与应用, 2009, 26(6): 629-633.
	FU Xiaofeng and WEI Wei. Facial expression recognition based on multi-scale centralized binary pattern[J]. Control Theory & Applications, 2009, 26(6): 629-633.
[3]	UÇAR A, DEMIR Y, and GÜZELI C. A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering[J]. Neural Computing and Applications, 2016, 27(1): 131-142. doi: 10.1007/s00521-014-1569-1.
[4]	TAN H C, ZHANG Y J, HAO C, et al. Person-independent expression recognition based on person-similarity weighted expression feature[J]. Journal of Systems Engineering and Electronics, 2010, 21(1): 118-126. doi: 10.3969/j.issn.1004- 4132.2010.01.019.
[5]	ZHANG S, ZHAO X, and LEI B. Facial expression recognition based on local binary patterns and local fisher discriminant analysis[J]. WSEAS Transactions on Signal Processing, 2012, 8(1): 21-31.
[6]	WANG Z, RUAN Q, and AN G. Facial expression recognition using sparse local Fisher discriminant analysis[J]. Neurocomputing, 2016, 174(174): 756-766. doi: 10.1016/ j.neucom.2015.09.083.
[7]	ZHAO X and ZHANG S. Facial expression recognition based on local binary patterns and kernel discriminant isomap[J]. Sensors, 2011, 11(10): 9573-9588. doi: 10.3390/s111009573.
[8]	CHEN J, TAKIGUCHI T, and ARIKI Y. Rotation-reversal invariant HOG cascade for facial expression recognition[J]. Signal Image & Video Processing, 2017(1-3): 1-8. doi: 10.1007 /s11760-017-1111-x.
[9]	HEGDE G, SEETHA M, and HEGDE N. Facial expression recognition using entire gabor filter matching score level fusion approach based on subspace methods[J]. Microelectronics Reliability, 2015, 52(3): 497-502. doi: 10.1007/978-3-319-26832-3_6.
[10]	ZHOU H, LAM K M, and HE X. Shape-appearance- correlated active appearance model[J]. Pattern Recognition, 2016, 56(C): 88-99. doi: 10.1016/j.patcog.2016.03.002.
[11]	OJALA T, PIETIKÄINEN M, and HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern recognition, 1996, 29(1): 51-59. doi: 10.1016/0031-3203(95)00067-4.
[12]	HEIKKILÄ M, PIETIKÄINEN M, and SCHMID C. Description of interest regions with local binary patterns[J]. Pattern Recognition, 2009, 42(3): 425-436. doi: 10.1016/ j.patcog.2008.08.014.
[13]	王晓华, 李瑞静, 胡敏, 等. 融合局部特征的面部遮挡表情识别[J]. 中国图象图形学报, 2016, 21(11): 1473-1482. doi: 10.11834/jig.20161107.
	WANG Xiaohua, LI Ruijing, HU Min, et al. Occluded facial expression recognition based on the fusion of local features[J]. Journal of Image and Graphics, 2016, 21(11): 1473-1482. doi: 10.11834/jig.20161107.
[14]	杨利平, 辜小花. 用于人脸识别的相对梯度直方图特征描述[J]. 光学精密工程, 2014, 22(1): 152-159. doi: 10.3788/OPE. 20142201.0152.
	YANG Liping and GU Xiaohua. Relative gradient histogram features for face recognition[J]. Optics and Precision Engineering, 2014, 22(1): 152-159. doi: 10.3788/OPE. 20142201.0152.
[15]	刘帅师, 田彦涛, 万川. 基于Gabor多方向特征融合与分块直方图的人脸表情识别方法[J]. 自动化学报, 2011, 37(12): 1455-1463. doi: 10.3724/SP.J.1004.2011.01455.
	LIU Shuishi, TIAN Yantao, and WAN Chuan. Facial expression recognition method based on gabor multi- orientation features fusion and block histogram[J]. Acta Automatica Sinica, 2011, 37(12): 1455-1463. doi: 10.3724 /SP.J.1004.2011.01455.
[16]	TURAN C and LAM K M. Region-based feature fusion for facial-expression recognition[C]. IEEE International Conference on Image Processing, Paris, 2014: 5966-5970. doi: 10.1109/ICIP.2014.7026204.
[17]	易积政, 毛峡, ISHIZUKA M, 等. 基于特征点矢量与纹理形变能量参数融合的人脸表情识别[J]. 电子与信息学报, 2013, 35(10): 2403-2410. doi: 10.3724/SP.J.1146.2012.01569.
	YI Jizheng, MAO Xia, ISHIZUKA M, et al. Facial expression recognition based on feature point vector and texture deformation energy parameters[J]. Journal of Electronics & Information Technology, 2013, 35(10): 2403-2410. doi: 10.3724/SP.J.1146.2012.01569.
[18]	胡敏, 江河, 王晓华, 等. 基于几何和纹理特征的表情层级分类方法[J]. 电子学报, 2017, 45(1): 164-172. doi: 10.3969/ j.issn.0372-2112.2017.01.023.
	HU Min, JIANG He, WANG Xiaohua, et al. A hierarchical classification method of expressions based on geometric and texture features[J]. Acta Electronica Sinica, 2017, 45(1): 164-172. doi: 10.3969/j.issn.0372-2112.2017.01.023.
[19]	孙权森, 曾生根, 王平安, 等. 典型相关分析的理论及其在特征融合中的应用[J]. 计算机学报, 2005, 28(9): 1524-1533. doi: 10.3321/j.issn:0254-4164.2005.09.015.
	SUN Quansen, ZENG Shenggen, WANG Pingan, et al. The theory of canonical correlation analysis and its application to feature fusion[J]. Chinese Journal of Computers, 2005, 28(9): 1524-1533. doi: 10.3321/j.issn:0254-4164.2005.09.015.