Frontal View Gait Recognition with Fusion of Depth Features from a Time of Flight Camera

Afendi, Tengku, Kurugollu, Fatih, Crookes, Danny, Bouridane, Ahmed and Farid, Mohsen (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. ISSN 1556-6013

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Afendi et al - Frontal View Gait Recognition with Fusion of Depth Features from a Time of Flight Camera AAM.pdf - Accepted Version

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Official URL: http://doi.org/10.1109/TIFS.2018.2870594

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%.

Item Type: Article
Uncontrolled Keywords: Gait recognition, frontal view, Time of Flight camera, fusion of features, depth gait data set
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 01 Aug 2018 08:57
Last Modified: 09 Jan 2019 16:00
URI: http://nrl.northumbria.ac.uk/id/eprint/35173

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