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

Journal article


Afendi Tengku Mohd, Kurugollu, Fatih, Crookes, Danny, Bouridane, Ahmed and Farid, Mohsen 2018. Frontal view gait recognition with fusion of depth features from a time of flight camera. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2018.2870594
AuthorsAfendi Tengku Mohd, Kurugollu, Fatih, Crookes, Danny, Bouridane, Ahmed and Farid, Mohsen
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%.

KeywordsGait Recognition; Frontal View; Time of Flight Camera; Fusion of features; depth gait data set
Year2018
JournalIEEE Transactions on Information Forensics and Security
PublisherIEEE
ISSN1556-6013
Digital Object Identifier (DOI)https://doi.org/10.1109/TIFS.2018.2870594
Web address (URL)http://hdl.handle.net/10545/623612
hdl:10545/623612
Publication dates17 Sep 2018
Publication process dates
Deposited18 Mar 2019, 13:12
Accepted02 Sep 2018
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© © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ContributorsQueen's University, Belfast, University of Derby and Northumbria University
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