Gait recognition using gait entropy image

Gait as a behavioural biometric is concerned with how people walk. However, most existing gait representations capture both motion and appearance information. They are thus sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, a novel gait representation...

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Bibliographic Details
Published in:3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009) p. P2
Main Authors: Bashir, K, Tao Xiang, Shaogang Gong
Format: Conference Proceeding
Language:English
Published: Stevenage IET 2009
Subjects:
Computer vision and image processing techniques
Optical, image and video signal processing
behavioural biometric
gait recognition
entropy
image representation
SOTON dataset
gait entropy image
silhouette images
biomimetics
gait analysis
gait representation
motion information
ISBN:1849192073, 9781849192071
Online Access:Get full text
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Summary:Gait as a behavioural biometric is concerned with how people walk. However, most existing gait representations capture both motion and appearance information. They are thus sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, a novel gait representation termed as Gait Entropy Image (GEnI) is proposed. Based on computing entropy, a GEnI encodes in a single image the randomness of pixel values in the silhouette images over a complete gait cycle. It thus captures mostly motion information and is robust to covariate condition changes that affect appearance. Extensive experiments on the USF HumanID dataset, CASIA dataset and the SOTON dataset have been carried out to demonstrate that the proposed gait representation outperforms existing methods, especially when there are significant appearance changes. Our experiments also show clear advantage of GEnI over the alternatives without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. (6 pages)
ISBN:1849192073
9781849192071
DOI:10.1049/ic.2009.0230