By Topic

Joint Subspace Learning for View-Invariant 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

3 Author(s)
Nini Liu ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Jiwen Lu ; Yap-Peng Tan

We propose in this paper a novel joint subspace learning (JSL) method for view-invariant gait recognition. Inspired by the finding that if a 3-D object can be well represented by the weighted sum of a sufficiently small number of prototypes in the same view, then the representation coefficients are generally consistent across different views, we propose to use these coefficients as view-invariant features for gait recognition. Firstly, we conduct JSL to obtain the prototypes of different views. Then, we represent each sample in both the gallery set and probe set acquired from different views as a linear combination of these prototypes in the corresponding views, and extract the coefficients for feature representation. Lastly, we perform recognition by using a simple nearest neighbor rule. Experimental results on the widely used CASIA-B gait database demonstrate the effectiveness of the proposed method.

Published in:

Signal Processing Letters, IEEE  (Volume:18 ,  Issue: 7 )
Biometrics Compendium, IEEE