Automated Gait Trait Analysis and Applications
Date
2018-09-20
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Abstract
The 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.
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Keywords
Gait, Kinect, Dynamic Bayesian network, Biometrics, Human Identification
Citation
Kozlow, P. (2018). Automated Gait Trait Analysis and Applications (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/33062