Facial expression recognition based on Gabor Parameters Matrix and improved Adaboost

doi:10.11772/j.issn.1001-9081.2014.04.1134

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (4): 1134-1138.DOI: 10.11772/j.issn.1001-9081.2014.04.1134

• Artificial intelligence • Previous Articles Next Articles

Facial expression recognition based on Gabor Parameters Matrix and improved Adaboost

YANG Fan,ZHANG Lei

College of Software Engineering, Lanzhou Institute of Technology, Lanzhou Gansu 730050, China

Received:2013-10-14 Revised:2013-12-27 Online:2014-04-29 Published:2014-04-01
Contact: YANG Fan

基于Gabor参数矩阵与改进Adaboost的人脸表情识别

杨凡,张磊

兰州工业学院软件工程学院,兰州 730050

通讯作者: 杨凡
作者简介:杨凡(1982-),男,湖北竹溪人,助教,硕士,主要研究方向:人工智能、模式识别;
张磊(1979-),男,河南偃市人,讲师,硕士研究生,主要研究方向:智能信息处理。
基金资助:
兰州工业学院青年科技创新基金资助项目

Abstract

Abstract:

To solve the problems of high-dimensionality, giant-computation and redundancy of Gabor features in current Facial Expression Recognition (FER), a new FER algorithm based on Gabor Parameter Matrix (GPM) and improved Adaboost was proposed. Firstly, the GPM was defined by combining pixel information of image and parameters of Gabor wavelet kernel; Secondly, the idea of Genetic Algorithm (GA) was introduced into Adaboost to improve its searching performance, then the improved Adaboost was used to select optimal features corresponding to the elements in GPM to build strong classifiers, thereby the dimensionalities, redundancy and calculation amount of Gabor features could be reduced by feature selection; Finally, on the basis of building several strong classifiers, a multi-expressions classification algorithm was developed to implement FER. The experimental results on Matlab indicate that average expression recognition rate of the proposed algorithm is 89.67%, and the selection efficiency of optimal features is improved significantly.

摘要：

针对目前人脸表情识别(FER)中Gabor特征维数高、计算量大并且存在特征冗余的问题,提出一种基于Gabor参数矩阵与改进Adaboost的人脸表情识别算法。首先,结合图像像素信息与Gabor小波核的参数定义Gabor参数矩阵;其次,在Adaboost中引入遗传算法(GA)的思想改进其搜索性能,并利用改进的Adaboost选择与Gabor参数矩阵元素对应的最优特征来构建强分类器,从而通过特征选择的方法降低Gabor特征的维数和冗余，减少计算量;最后,在构建多个强分类器的基础上,提出多表情分类算法实现面部表情的分类识别。基于Matlab的实验结果表明,该算法的平均表情识别率为89.67%,且最优特征的选取效率得到明显提高。

CLC Number:

TP391.41

YANG Fan ZHANG Lei. Facial expression recognition based on Gabor Parameters Matrix and improved Adaboost[J]. Journal of Computer Applications, 2014, 34(4): 1134-1138.

杨凡张磊. 基于Gabor参数矩阵与改进Adaboost的人脸表情识别[J]. 计算机应用, 2014, 34(4): 1134-1138.

References

［1］JIANG B, JIA K, YANG G. Research advance of facial expression recognition ［J］. Computer Science, 2011, 38(4): 25-31.(蒋斌,贾克斌,杨国胜.人脸表情识别的研究进展［J］.计算机科学,2011,38(4):25-31.)
［2］WU T, FU S, YANG G. Survey of the facial expression recognition research ［C］// BICS 2012: Proceedings of the 2012 International Conference on Brain Inspired Cognitive Systems, LNCS 7366. Berlin: Springer, 2012: 392-402.
［3］DAUGMAN J G. Complete discrete 2-D Gabor transforms by neural networks form image analysis and compression ［J］. IEEE Transactions on Acoustics, Speech and Signal Processing, 1988, 36(7): 1169-1179.
［4］FAZLI S, AFROUZIAN R, SEYEDARABI H. High-performance facial expression recognition using Gabor filter and probabilistic neural network ［C］// ICIS 2009: Proceedings of the 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. Piscataway: IEEE, 2009: 93-96.
［5］LIU S, WANG L. Facial expression recognition algorithm based on local Gabor wavelet automatic segmentation ［J］. Journal of Computer Applications, 2009, 29(11): 3040-3043.(刘珊珊,王玲.基于自动分割的局部Gabor小波人脸表情识别算法［J］.计算机应用,2009,29(11):3040-3043.)
［6］ZHANG Z, ZHAO Z, YUAN T. Expression recognition based on 2D multi-scale block local Gabor binary patterns ［J］. Journal of Computer Applications, 2010, 30(4): 964-966.(张铮,赵政,袁甜甜.基于二维多尺度局部Gabor二进制模式特征的表情识别［J］.计算机应用,2010,30(4):964-966.)
［7］LIU L, KUANG G. Overview of image textural feature extractionmethods ［J］. Journal of Image and Graphics, 2009, 14(4): 622-635.(刘丽,匡纲要.图像纹理特征提取方法综述［J］.中国图象图形学报,2009,14(4):622-635.)
［8］ZHU J, SU G, LI Y. Facial expression recognition based on Gabor feature and Adaboost ［J］. Journal of Optoelectronics · Laser, 2006, 17(8): 993-998.(朱健翔,苏光大,李迎春.结合Gabor特征与Adaboost的人脸表情识别［J］.光电子·激光,2006,17(8),993-998.)
［9］FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting ［J］. Journal of Computer and System Sciences, 1997, 55(1): 119-139.
［10］YAO W, SUN Z, ZHANG Y. Optimal Gabor feature for facial expression recognition ［J］. Journal of Computer-Aided Design & Computer Graphics, 2008, 20(1): 79-84.(姚伟,孙正兴,张岩.面向脸部表情识别的Gabor特征选择方法［J］.计算机辅助设计与图形学学报,2008,20(1):79-84.)
［11］LI W, HE F, QIAN Y, et al.Facial expression recognition based on Adaboost-Gaussian process classification ［J］. Journal of Zhejiang University: Engineering Science, 2012, 46(1): 79-83.(李文书,何芳芳,钱沄涛,等.基于Adaboost-高斯过程分类的人脸表情识别［J］.浙江大学学报:工学版,2012,46(1):79-83.)
［12］OWUSU E, ZHAN Y, MAO Q R. A neural-Adaboost based facial expression recognition system ［J］. Expert System with Applications, 2014, 41(7): 3383-3390.
［13］BIAN X, MI L. Development on genetic algorithm theory and its applications ［J］. Application Research of Computers, 2010, 27(7): 2425-2434.(边霞,米良.遗传算法理论及其应用研究进展［J］.计算机应用研究,2010,27(7):2425-2434.)
［14］LYONS M, AKAMATSU S, KAMACHI M, et al.Coding facial expressions with Gabor wavelets ［C］// FG '98: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition. Washington, DC: IEEE Computer Society, 1998: 200-205.

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Facial expression recognition based on Gabor Parameters Matrix and improved Adaboost

基于Gabor参数矩阵与改进Adaboost的人脸表情识别

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