Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105996
Title: Free-view gait recognition
Authors: Tian, Yonghong
Wei, Lan
Lu, Shijian
Huang, Tiejun
Keywords: Gait Analysis
Walking
DRNTU::Engineering::Computer science and engineering
Issue Date: 2019
Source: Tian, Y., Wei, L., Lu, S., & Huang, T. (2019). Free-view gait recognition. PLOS ONE, 14(4), e0214389-. doi:10.1371/journal.pone.0214389
Series/Report no.: PLOS ONE
Abstract: Human gait has been shown to be an effective biometric measure for person identification at a distance. On the other hand, changes in the view angle pose a major challenge for gait recognition as human gait silhouettes are usually different from different view angles. Traditionally, such a multi-view gait recognition problem can be tackled by View Transformation Model (VTM) which transforms gait features from multiple gallery views to the probe view so as to evaluate the gait similarity. In the real-world environment, however, gait sequences may be captured from an uncontrolled scene and the view angle is often unknown, dynamically changing, or does not belong to any predefined views (thus VTM becomes inapplicable). To address this free-view gait recognition problem, we propose an innovative view-adaptive mapping (VAM) approach. The VAM employs a novel walking trajectory fitting (WTF) to estimate the view angles of a gait sequence, and a joint gait manifold (JGM) to find the optimal manifold between the probe data and relevant gallery data for gait similarity evaluation. Additionally, a RankSVM-based algorithm is developed to supplement the gallery data for subjects whose gallery features are only available in predefined views. Extensive experiments on both indoor and outdoor datasets demonstrate that the VAM outperforms several reference methods remarkably in free-view gait recognition.
URI: https://hdl.handle.net/10356/105996
http://hdl.handle.net/10220/48842
DOI: 10.1371/journal.pone.0214389
Rights: ©2019 Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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