Speed-invariant gait recognition based on Procrustes Shape Analysis using higher-order shape configuration
Date
2011
Authors
Kusakunniran, Worapan
Wu, Qiang
Zhang, Jian
Li, Hongdong
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IEEE Signal Processing Society
Abstract
Walking speed change is considered a typical challenge hindering reliable human gait recognition. This paper proposes a novel method to extract speed-invariant gait feature based on Procrustes Shape Analysis (PSA). Two major components of PSA, i.e., Procrustes Mean Shape (PMS) and Procrustes Distance (PD), are adopted and adapted specifically for the purpose of speed-invariant gait recognition. One of our major contributions in this work is that, instead of using conventional Centroid Shape Configuration (CSC) which is not suitable to describe individual gait when body shape changes particularly due to change of walking speed, we propose a new descriptor named Higher-order derivative Shape Configuration (HSC) which can generate robust speed-invariant gait feature. From the first order to the higher order, derivative shape configuration contains gait shape information of different levels. Intuitively, the higher order of derivative is able to describe gait with shape change caused by the larger change of walking speed. Encouraging experimental results show that our proposed method is efficient for speed-invariant gait recognition and evidently outperforms other existing methods in the literatures.
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Keywords: Body shapes; Descriptors; First order; Gait features; Gait recognition; Higher order; Human gait; Human identification; Procrustes distance; Procrustes mean shape; Procrustes shape analysis; Shape change; Shape information; speed-invariant; Walking speed; Gait recognition; human identification; procrustes shape analysis; speed-invariant
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Speed-invariant gait recognition based on Procrustes Shape Analysis using higher-order shape configuration
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Conference paper
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2037-12-31
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