|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 recognitionfacial expressionfeature extractionkernel 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:

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Last Update: 2018-11-19