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    Frontal view gait recognition with fusion of depth features from a time of flight camera

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    Authors
    Afendi Tengku Mohd
    Kurugollu, Fatih
    Crookes, Danny
    Bouridane, Ahmed
    Farid, Mohsen
    Affiliation
    Queen's University, Belfast
    University of Derby
    Northumbria University
    Issue Date
    2018-09-17
    
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    Abstract
    Frontal view gait recognition for people identification has been carried out using single RGB, stereo RGB, Kinect 1.0 and Doppler radar. However, existing methods based on these camera technologies suffer from several problems. Therefore, we propose a four-part method for frontal view gait recognition based on fusion of multiple features acquired from a Time of Flight (ToF) camera. We have developed a gait data set captured by a ToF camera. The data set includes two sessions recorded seven months apart, with 46 and 33 subjects respectively, each with six walks with five covariates. The four-part method includes: a new human silhouette extraction algorithm that reduces the multiple reflection problem experienced by ToF cameras; a frame selection method based on a new gait cycle detection algorithm; four new gait image representations; and a novel fusion classifier. Rigorous experiments are carried out to compare the proposed method with state-of-the-art methods. The results show distinct improvements over recognition rates for all covariates. The proposed method outperforms all major existing approaches for all covariates and results in 66.1% and 81.0% Rank 1 and Rank 5 recognition rates respectively in overall covariates, compared with a best state-of-the-art method performance of 35.7% and 57.7%.
    Citation
    Zulcaffle, T.M.A., Kurugollu, F., Crookes, D., Bouridane, A. and Farid, M., (2019). 'Frontal View Gait Recognition With Fusion of Depth Features From a Time of Flight Camera'. IEEE Transactions on Information Forensics and Security, 14(4), pp.1067-1082. DOI: 10.1109/TIFS.2018.2870594
    Publisher
    IEEE
    Journal
    IEEE Transactions on Information Forensics and Security
    URI
    http://hdl.handle.net/10545/623612
    DOI
    10.1109/TIFS.2018.2870594
    Additional Links
    https://ieeexplore.ieee.org/abstract/document/8466800
    Type
    Article
    Language
    en
    ISSN
    1556-6013
    ae974a485f413a2113503eed53cd6c53
    10.1109/TIFS.2018.2870594
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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