Yanushkevich, Svetlana N.Kozlow, Patrick2018-10-012018-10-012018-09-20http://hdl.handle.net/1880/108709The main focus of this thesis is the development and feasibility testing of a proposed gait biometric screening system based on the Kinect v2 sensor. To achieve contactless gait biometric extraction the system uses a virtual Kinect 3D skeleton to construct models in real time. These models are then used to identify an individual's gait characteristics. The features found from the virtual models are passed through a gait recognition system which provides insight into what type of gait pattern is being observed by the camera. Extensive experiments with different classification methods such as Support Vector Machines, K-Nearest-Neighbors, and Dynamic Bayesian Networks are tested to determine the effectiveness of the system. The proposed gait recognition network is tested using locally collected and publically available databases to validate the results and prove that the system is feasible.engUniversity 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.GaitKinectDynamic Bayesian networkBiometricsHuman IdentificationArtificial IntelligenceEngineering--BiomedicalEngineering--Electronics and Electricalmaster thesis10.11575/PRISM/33062