Kinect-based Gait Recognition Using Deep Learning

Bari, A S M Hossain

Kinect-based Gait Recognition Using Deep Learning

dc.contributor.advisor	Gavrilova, Marina L.
dc.contributor.author	Bari, A S M Hossain
dc.contributor.committeemember	Jacobson, Michael J.
dc.contributor.committeemember	Korobenko, Artem
dc.date	2021-02
dc.date.accessioned	2020-12-02T21:41:49Z
dc.date.available	2020-12-02T21:41:49Z
dc.date.issued	2020-11-27
dc.description.abstract	Accurate gait recognition is of high significance for numerous industrial and consumer applications, including finance, virtual reality, online games, medical rehabilitation, collaborative space exploration, and others. This thesis proposes two novel deep learning architectures for a highly accurate and robust Kinect-based gait recognition. First, the architecture of the Deep Learning Neural Network (DLNN) is developed using two unique view and pose invariant geometric features. Second, the end-to-end training of a residual learning-based convolutional neural network, named KinectGaitNet, is proposed to enable even higher recognition performance without the necessity of extracting domain-specific handcrafted features. The performance of the DLNN architecture and KinectGaitNet are evaluated on two publicly available 3D skeleton-based gait datasets recorded with the Microsoft Kinect sensor. It is experimentally proven that the accuracy, precision, recall, and F-score of the DLNN architecture and KinectGaitNet are superior to the recent state-of-the-art methods for the Kinect skeleton-based gait recognition.	en_US
dc.identifier.citation	Bari, A. S. M. Hossain. (2020). Kinect-based Gait Recognition Using Deep Learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/38415
dc.identifier.uri	http://hdl.handle.net/1880/112800
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Gait Recognition	en_US
dc.subject	Deep Learning	en_US
dc.subject	Microsoft Kinect	en_US
dc.subject	Residual Neural Network	en_US
dc.subject	Behavioral Biometric	en_US
dc.subject	Deep Convolutional Neural Network	en_US
dc.subject.classification	Computer Science	en_US
dc.title	Kinect-based Gait Recognition Using Deep Learning	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Computer Science	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US