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Hierarchical and multi-featured fusion for effective gait recognition under variable scenarios

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Abstract

Human identification by gait analysis has attracted a great deal of interest in the computer vision and forensics communities as an unobtrusive technique that is capable of recognizing humans at range. In recent years, significant progress has been made, and a number of approaches capable of this task have been proposed and developed. Among them, approaches based on single source features are the most popular. However, the recognition rate of these methods is often unsatisfactory due to the lack of information contained in single feature sources. Consequently, in this paper, a hierarchal and multi-featured fusion approach is proposed for effective gait recognition. In practice, using more features for fusion does not necessarily mean a better recognition rate and features should in fact be carefully selected such that they are complementary to each other. Here, complementary features are extracted in three groups: Dynamic Region Area; Extension and Space features; and 2D Stick Figure Model features. To balance the proportion of features used in fusion, a hierarchical feature-level fusion method is proposed. Comprehensive results of applying the proposed techniques to three well-known datasets have demonstrated that our fusion-based approach can improve the overall recognition rate when compared to a benchmark algorithm.

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Acknowledgments

This paper is partially supported by the Research Innovation Fund, Education Department of Shaanxi Province and National Natural Science Foundation of China (61272381). The authors also wish to thank the editors and anonymous reviewers for their valuable and constructive comments in further improving the quality of this paper.

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Authors and Affiliations

  1. School of Information, Central University of Finance and Economics (CUFE), Beijing, China

    Yanmei Chai & Yang Li

  2. College of Electronics and Information, Xi’an Polytechnic University, Xi’an, China

    Jie Ren

  3. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  4. Centre for excellence in Signal and Image Processing, University of Strathclyde, Glasgow, UK

    Jinchang Ren & Paul Murray

Authors
  1. Yanmei Chai
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  2. Jie Ren
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  3. Huimin Zhao
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  4. Yang Li
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  5. Jinchang Ren
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  6. Paul Murray
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Correspondence to Jinchang Ren.

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Chai, Y., Ren, J., Zhao, H. et al. Hierarchical and multi-featured fusion for effective gait recognition under variable scenarios. Pattern Anal Applic 19, 905–917 (2016). https://doi.org/10.1007/s10044-015-0471-5

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  • DOI: https://doi.org/10.1007/s10044-015-0471-5

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