An adaptive system for gait recognition in multi-view environments

Yu Guan, Chang Tsun Li, Yongjian Hu

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

    6 Citations (Scopus)


    Gait recognition systems often suffer from the challenges when query gaits are under the coupled effects of unknown view angles and large intra-class variations (e.g., wearing a coat). In this paper, we deem it as a two-stage classification problem, namely, view detection and fixed-view gait recognition. First, we propose two simple yet effective feature types (i.e., global features and local features) for view detection. By using the detected view information, the corresponding gallery (i.e., enrolled gait) for the detected view can be adaptively selected to perform the fixed-view gait recognition. For fixed-view gait recognition, since the inter-class variations for training are normally small, whereas the query gait usually has large intra-class variations, random subspace method are adopted. We evaluate our approach on the largest multi-view gait database CASIA-B dataset. The avoidance of searching whole multi-view database as well as the competitive performance indicate that our proposed method is practical for gait recognition in real world surveillance scenarios.

    Original languageEnglish
    Title of host publicationMM&Sec'12
    Subtitle of host publicationProceedings of the 14th ACM Multimedia and Security Workshop
    Place of PublicationNew York, USA
    PublisherACM Press
    Number of pages6
    ISBN (Print)9781450314183
    Publication statusPublished - 2012
    Event14th ACM Multimedia and Security Workshop: MMSEC 2012 - University of Warwick, Coventry, United Kingdom
    Duration: 06 Sep 201207 Sep 2012


    Conference14th ACM Multimedia and Security Workshop
    Country/TerritoryUnited Kingdom
    Internet address


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