基于改进的局部方向模式人脸表情识别算法
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摘要
针对LDP利用Kirsch算子计算8方向的边缘响应值并排序,特征提取速度慢的问题,提出了一种改进的分解局部方向模式DLDP(divided local directional pattern)特征提取方法。将Kirsch算子的8个方向掩模分成2个子方向掩模再分别计算边缘响应值,获得2个编码(DLDP1和DLDP2),级联两个编码的直方图得到表情特征DLDP。然后利用主成分分析法(PCA,principal component analysis)降维处理。最后用支持向量机进行表情识别,在JAFFE数据库上的实验表明,本文方法与近几年效果较好的特征提取算法相比,不仅缩短了特征提取的运算时间,而且提高了识别率。
The local directional pattern(LDP)descriptor is a method for texture feature extraction.It calculates and sorts edge response values of eight different directions,thus the speed is slower than other local texture feature extraction algorithm.This paper presents a new feature descriptor called divided local directional pattern(DLDP)for feature extraction.In this method,Kirsch masks in eight different orientations were divided into two sub-directional masks.The edge response values were calculated respectively to obtain DLDP1 and DLDP2.DLDP1 and DLDP2 were concatenated into a single DLDP descriptor.Then principal component analysis(PCA) was used for dimensionality reduction processing.Finally,the support vector machine(SVM)was applied to classify and recognize facial expression.The experimental results show that compared with the better feature extraction algorithms in recent years,the improved local direction pattern can not only reduce the computation time,but also improve the rate of facial expression recognition.
The local directional pattern(LDP)descriptor is a method for texture feature extraction.It calculates and sorts edge response values of eight different directions,thus the speed is slower than other local texture feature extraction algorithm.This paper presents a new feature descriptor called divided local directional pattern(DLDP)for feature extraction.In this method,Kirsch masks in eight different orientations were divided into two sub-directional masks.The edge response values were calculated respectively to obtain DLDP1 and DLDP2.DLDP1 and DLDP2 were concatenated into a single DLDP descriptor.Then principal component analysis(PCA) was used for dimensionality reduction processing.Finally,the support vector machine(SVM)was applied to classify and recognize facial expression.The experimental results show that compared with the better feature extraction algorithms in recent years,the improved local direction pattern can not only reduce the computation time,but also improve the rate of facial expression recognition.
引文
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[15]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.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.(in Chinese)
[16]颜雪军,赵春霞,袁夏.2DPCA-SIFT:一种有效的局部特征描述方法[J].自动化学报,2014,40(4):675-682.YAN Xuejun,ZHAO Chunxia,YUAN Xia.2DPCA-SIFT:an efficient local feature descriptor[J].Acta Automatica Sinica,2014,40(4):675-682.(in Chinese)
[17]Jian Z,Luxi H,Jian J,et al.A fast iterative pursuit algorithm in robust face recognition based on sparse representation[J].Mathematical Problems in Engineering,2014,2014(2):1-11.
[2]Tasnim T,Paul A,et al.Human expression recognition based on facial features[C]∥Electronics and Vision,IEEE Computer Society Conference on.US:IEEE,2016:990-994.
[3]张友梅,张伟.基于数据融合的表情识别[J].四川大学学报(工程科学版),2016,48(6):160-164.ZHANG Youmei,ZHANG Wei.Facial expression recognition based on data fusion[J].Journal of Sichuan University(Engineering Science Edition),2016,48(6):160-164.(in Chinese)
[4]Jabid T,Kabir M H,Chae O.Facial expression recognition using local directional pattern(LDP)[J].2010,119(5):1605-1608.
[5]Rivera A R,Castillo J R,Chae O.Local directional texture pattern image descriptor[J].Pattern Recognition Letters,2015,51(1):94-100.
[6]Srinivasa P R,Chandra Mouli PVS.Dimensionality reduced local directional pattern(DR-LDP)for face recognition[J].Expert Systems with Applications,2016,63:66-73.
[7]Ryu B,Rivera A R,Kim J,et al.Local directional ternary pattern for facial expression recognition[J].IEEE Transactions on Image Processing,2017,PP(99):1-4.
[8]Filho J B O S,Diniz P S R.A fixed-point online kernel principal component extraction algorithm[J].IEEE Transactions on Signal Processing,2017,PP(99):12-16.
[9]JUN-BAI L B.an algorithm for edge extraction based on fast kirsch and the probability of being true edges[J].Computer Applications,2001,21(2):33-35.
[10]Ren Y,Liao L,Maybank S J,et al.Hyperspectral image spectral-spatial feature extraction via tensor principal component analysis[J].IEEE Geoscience&Remote Sensing Letters,2017,PP(99):1-5.
[11]Mlakar U,Fister I,Brest J,et al.Multi-objective differential evolution for feature selection in facial expression recognition systems[J].Expert Systems with Applications,2017,89.
[12]解仑,卢亚楠,姜波,等.基于人脸运动单元及表情关系模型的自动表情识别[J].北京理工大学学报,2016,36(2):163-169.XIE Lun,LU Yanan,JING Bo,et al.Expression automatic recognition based on facial action units and expression relationship model[J].Transactions of Beijing Institute of Technology,2016,36(2):163-169.(in Chinese)
[13]Uddin M Z.A depth video-based facial expression recognition system utilizing generalized local directional deviation-based binary pattern feature discriminant analysis[J].Multimedia Tools and Applications,2016,75(12):1-16.
[14]Alphonse A S,Dharma D.Enhanced Gabor(E-Gabor),Hypersphere-based normalization and pearson general kernelbased discriminant analysis for dimension reduction and classification of facial emotions[J].Expert Systems with Applications,2017,90.
[15]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.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.(in Chinese)
[16]颜雪军,赵春霞,袁夏.2DPCA-SIFT:一种有效的局部特征描述方法[J].自动化学报,2014,40(4):675-682.YAN Xuejun,ZHAO Chunxia,YUAN Xia.2DPCA-SIFT:an efficient local feature descriptor[J].Acta Automatica Sinica,2014,40(4):675-682.(in Chinese)
[17]Jian Z,Luxi H,Jian J,et al.A fast iterative pursuit algorithm in robust face recognition based on sparse representation[J].Mathematical Problems in Engineering,2014,2014(2):1-11.