[1]BEN Xianye,WANG Kejun,LIU Haiyang.A comparative study on kernel methods and their applications to gait recognition[J].CAAI Transactions on Intelligent Systems,2011,6(1):63-67.
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A comparative study on kernel methods and their applications to gait recognition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 1 Page number: 63-67 Column: 学术论文—机器感知与模式识别 Public date: 2011-02-25
Title:
A comparative study on kernel methods and their applications to gait recognition
Author(s):
BEN Xianye1;  WANG Kejun2;  LIU Haiyang1
1.School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China；
?2.College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
gait recognition;  kernel principal component analysis (KPCA);  kernel linear discriminant analysis (KLDA);  kernel two dimensional principal component analysis (K2DPCA);  kernel two dimensional linear discriminant analysis (K2DLDA)
CLC:
TP391.41
DOI:
-
Abstract:
A kernel trick was applied to gait recognition in order to improve recognition performance. A novel solution was proposed for kernel two dimensional linear discriminant analysis. Feature extraction, which makes use of kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA), kernel two dimensional principal component analysis (K2DPCA), and kernel two dimensional linear discriminant analysis (K2DLDA), was performed for contrasting experiments in HEU(B)’s locally built gait database. The experimental results demonstrate that a kernel trick applied to a matrix form is more efficient than in vector form. K2DPCA outperforms KPCA significantly with a single sample per person, and K2DLDA has the advantage of less time spent on recognition testing.
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