Random subspace method for gait recognition

Yu Guan, Chang Tsun Li, Yongjian Hu

    Research output: Book chapter/Published conference paperConference paperpeer-review

    17 Citations (Scopus)
    5 Downloads (Pure)


    Over fitting is a common problem for gait recognition algorithms when gait sequences in gallery for training are acquired under a single walking condition. In this paper, we propose an approach based on the random subspace method (RSM) to address such over learning problems. Initially, two-dimensional Principle Component Analysis (2DPCA) is adopted to obtain the full hypothesis space (i.e., eigen space). Multiple inductive biases (i.e., subspaces) are constructed, each with the corresponding basis vectors randomly chosen from the initial eigen space. This procedure can not only largely avoid over adaptation but also facilitate dimension reduction. The final classification is achieved by the decision committee which follows a majority voting criterion from the labeling results of all the subspaces. Experimental results on the benchmark USF Human ID gait database show that the proposed method is a feasible framework for gait recognition under unknown walking conditions.
    Original languageEnglish
    Title of host publicationProceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages6
    ISBN (Print)9780769547299
    Publication statusPublished - 2012
    EventIEEE International Conference on Multimedia and Expo (ICME 2012) - Melbourne Convention and Exhibition Centre, Melbourne, Australia
    Duration: 09 Jul 201213 Jul 2012
    https://web.archive.org/web/20120504013843/http://www.icme2012.org/index.php (Archived page)


    ConferenceIEEE International Conference on Multimedia and Expo (ICME 2012)
    Internet address


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