|Table of Contents|

Kernel Partial Least Squares Regression with Applicationsto Facial Expression Recognition(PDF)

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

Issue:
2018年03期
Page:
14-
Research Field:
·人工智能算法与应用专栏·
Publishing date:

Info

Title:
Kernel Partial Least Squares Regression with Applicationsto Facial Expression Recognition
Author(s):
Zhu Jiong
School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221000,China
Keywords:
face recognition; facial expression; feature extraction; kernel partial least squares regression
PACS:
TP183
DOI:
10.3969/j.issn.1001-4616.2018.03.003
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.

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.

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Last Update: 2018-11-19