[1]朱 炯.核偏最小二乘回归在面部表情识别中的应用[J].南京师范大学学报(自然科学版),2018,41(03):14.[doi:10.3969/j.issn.1001-4616.2018.03.003]
　Zhu Jiong.Kernel Partial Least Squares Regression with Applicationsto Facial Expression Recognition[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(03):14.[doi:10.3969/j.issn.1001-4616.2018.03.003]
点击复制

核偏最小二乘回归在面部表情识别中的应用()
分享到：

《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第41卷
期数:
2018年03期
页码:
14
栏目:
·人工智能算法与应用专栏·
出版日期:
2018-09-30

文章信息/Info

Title:
Kernel Partial Least Squares Regression with Applicationsto Facial Expression Recognition
文章编号:
1001-4616(2018)03-0014-08
作者:
朱 炯
中国矿业大学计算机科学与技术学院,江苏 徐州 221000
Author(s):
Zhu Jiong
School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221000,China
关键词:
人脸识别; 面部表情; 特征提取; 核偏最小二乘回归
Keywords:
face recognition; facial expression; feature extraction; kernel partial least squares regression
分类号:
TP183
DOI:
10.3969/j.issn.1001-4616.2018.03.003
文献标志码:
A
摘要:
面部表情识别作为一个分类问题,由于其在人机交互中扮演着重要角色,已成为人脸识别领域的前沿研究方向. 本文分析了(核)偏最小二乘回归并获得了一种新方法来解决这些问题. 此外还发现核偏最小二乘回归的第一阶段等价于特征提取的广义判别分析并证明了核偏最小二乘回归可以通过定义虚拟矩阵直接应用于分类问题. 通过实验发现,提出的线性降维改进算法在大多数情况下优于其他常用算法; 基于面部表情识别的核偏最小二乘回归算法在人脸数据集上也取得了良好的分类效果.
Abstract:
As a classification problem,facial expression recognition has been playing an important role in human-machine interaction,and has become a frontier research direction in face recognition field. This paper analyzes(kernel)partial least squares regression and obtains a new method to solve them. Moreover,it is of interest to note that the first stage of kernel partial least squares regression is equivalent to generalized discriminant analysis for feature extraction. At last,this paper also demonstrates that the kernel partial least squares regression can be directly applied to the classification problems by defining the dummy matrix. In conclusion,experimental results show that the proposed algorithm outperforms other linear dimensionality reduction algorithm commonly used in most cases; the kernel partial least squares regression algorithm based on facial expression recognition also achieves good classification performance.

参考文献/References:

[1] 徐琳琳,张树美,赵俊莉. 基于图像的面部表情识别方法综述[J]. 计算机应用,2017,37(12):3509-3516,3546.
[2]宫玉娇. 面部表情识别方法研究综述[J]. 产业与科技论坛,2016,15(13):57-58.
[3]杜柏圣. 卷积网络的无监督特征提取对人脸识别的研究[J]. 计算机技术与发展,2018(6):17-20.
[4]KAPIL J. Multiple feature descriptors based model for individual identification in group photos[J/OL]. [2018-04-06]. http://doi.org/10.1016/j.jksuci.2017.02.002.
[5]IKHLAS A Q,ALI K J. A hybrid feature extraction framework for face recognition:HOG and compressive sensing[J]. International journal of handheld computing research,2017,8(1):1-13.
[6]JIA Y,LUO Z. Summary of EMG feature extraction[J]. Chinese journal of electron devices,2007,30(1):326-330.
[7]HOWLAND P,PARK H. Generalizing discriminant analysis using the generalized singular value decomposition[J]. IEEE transactions on pattern analysis and machine intelligence,2004,26(8):995-1006.
[8]尚晓琳,张澜. 基于奇异值分解的一类广义特征值反问题[J]. 内蒙古工业大学学报(自然科学版),2017,36(1):1-5.
[9]ROSIPAL R,TREJO L J. Kernel partial least squares regression in reproducing kernel hilbert space[J]. Journal of machine learning research,2001,2(12):97-123.
[10]HELANDER E,SILéN H,VIRTANEN T,et al. Voice conversion using dynamic kernel partial least squares regression[J]. IEEE transactions on audio,speech,and language processing,2012,20(3):806-817.
[11]SRINIVASAN B V,LUO Y,GARCIA R D,et al. A symmetric kernel partial least squares framework for speaker recognition[J]. IEEE transactions on audio,speech,and language processing,2013,21(7):1415-1423.
[12]GUO G,MU G. Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression[C]//2011 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Colorado Springs,CO,USA:IEEE,2011:657-664.
[13]DHANJAL C,GUNN S R,SHAWE T J. Efficient sparse kernel feature extraction based on partial least squares[J]. IEEE transactions on pattern analysis and machine intelligence,2009,31(8):1347-1361.
[14]ROSIPAL R,TREJO L J,MATTHEWS B. Kernel PLS-SVC for linear and nonlinear classification[C]//Twentieth International Conference on Machine Learning. Washington DC,USA:AAAI,2003:640-647.
[15]BAUDAT G,ANOUAR F. Generalized discriminant analysis using a kernel approach[J]. Neural computation,2000,12(10):2385-2404.

相似文献/References:

[1]朱长水,丁 勇,袁宝华,等.融合LBP和LPQ的人脸识别[J].南京师范大学学报(自然科学版),2015,38(01):104.
　Zhu Changshui,Ding Yong,Yuan Baohua,et al.Face Recognition Based on Local Binary Patternand Local Phase Quantization[J].Journal of Nanjing Normal University(Natural Science Edition),2015,38(03):104.
[2]沈学华,詹永照,程显毅,等.基于样本融合的核稀疏人脸识别方法[J].南京师范大学学报(自然科学版),2016,39(04):0.[doi:10.3969/j.issn.1001-4616.2016.04.007]
　Shen Xuehua,Zhan Yongzhao,Cheng Xianyi,et al.A Kernel Sparse Representation Method Based onSamples Fusion for Face Recognition[J].Journal of Nanjing Normal University(Natural Science Edition),2016,39(03):0.[doi:10.3969/j.issn.1001-4616.2016.04.007]
[3]顼 聪,朱 毅,陶永鹏.面向方向选择的差值局部方向模式的人脸识别[J].南京师范大学学报(自然科学版),2020,43(04):113.[doi:10.3969/j.issn.1001-4616.2020.04.016]
　Xu Cong,Zhu Yi,Tao Yongpeng.Face Recognition Based on Direction-SelectedDifference Local Direction Pattern[J].Journal of Nanjing Normal University(Natural Science Edition),2020,43(03):113.[doi:10.3969/j.issn.1001-4616.2020.04.016]
[4]陈飞玥,朱玉莲,陈晓红.多层特征融合的PCANet及其在人脸识别中的应用[J].南京师范大学学报(自然科学版),2021,44(02):127.[doi:10.3969/j.issn.1001-4616.2021.02.018]
　Chen Feiyue,Zhu Yulian,Chen Xiaohong.Multi-stage Feature Fusion PCANet and Its Application to Face Recognition[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(03):127.[doi:10.3969/j.issn.1001-4616.2021.02.018]

备注/Memo

备注/Memo:
收稿日期:2018-04-16.
基金项目:国家自然科学基金(61672522).
通讯联系人:朱炯,硕士研究生,研究方向:机器学习. E-mail:804194762@qq.com
更新日期/Last Update: 2018-11-19