By Topic

Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis and its application to gait recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xianye Ben ; Sch. of Transp. Sci. & Eng., Harbin Inst. of Technol., Harbin, China ; Shi An ; Weixiao Meng ; Ze Wang

In this paper, a novel algorithm for feature extraction -Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) is proposed. The improved SpC2DLPPCA algorithm over C2DLPPCA and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefits greatly to three points: (1) SpC2DLPPCA can overcome a failing that larger dimension matrix may bring about more consuming time on computing its eigenvalues and eigenvectors. (2) SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact the expression of features. Finally, experiments on the CASIA(B) gait database show that SpC2DLPPCA has higher recognition accuracies than C2DLPPCA and SpC2DPCA.

Published in:

Communications and Networking in China (CHINACOM), 2011 6th International ICST Conference on

Date of Conference:

17-19 Aug. 2011