Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model

Worapan Kusakunniran*, Qiang Wu, Jian Zhang, Hongdong Li

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    64 Citations (Scopus)

    Abstract

    Gait has been known as an effective biometric feature to identify a person at a distance. However, variation of walking speeds may lead to significant changes to human walking patterns. It causes many difficulties for gait recognition. A comprehensive analysis has been carried out in this paper to identify such effects. Based on the analysis, Procrustes shape analysis is adopted for gait signature description and relevant similarity measurement. To tackle the challenges raised by speed change, this paper proposes a higher order shape configuration for gait shape description, which deliberately conserves discriminative information in the gait signatures and is still able to tolerate the varying walking speed. Instead of simply measuring the similarity between two gaits by treating them as two unified objects, a differential composition model (DCM) is constructed. The DCM differentiates the different effects caused by walking speed changes on various human body parts. In the meantime, it also balances well the different discriminabilities of each body part on the overall gait similarity measurements. In this model, the Fisher discriminant ratio is adopted to calculate weights for each body part. Comprehensive experiments based on widely adopted gait databases demonstrate that our proposed method is efficient for cross-speed gait recognition and outperforms other state-of-the-art methods.

    Original languageEnglish
    Article number6205650
    Pages (from-to)1654-1668
    Number of pages15
    JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
    Volume42
    Issue number6
    DOIs
    Publication statusPublished - 2012

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