Gavrilova, MarinaAhmed, Faisal2016-07-122016-07-1220162016Ahmed, F. (2016). Model-Based Gait and Action Recognition Using Kinect (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26931http://hdl.handle.net/11023/3122Being the very first in the category of low-cost consumer-level depth sensors, the recent release of Microsoft Kinect has opened the door to a new generation of computer vision and biometric security applications. This thesis focuses on designing new methodologies for Kinect-based gait and action recognition systems that utilize the Kinect 3D virtual skeleton to construct effective and robust motion representations. The proposed gait recognition method focuses on designing a feature descriptor that can capture person-specific distinct motion patterns, caused by the influence of human physiology and behavioral traits. On the other hand, the proposed action recognition method involves constructing a person-independent feature descriptor that can suppress person-specific motion traits while highlighting a more generic and high level description of action-specific skeletal joint movements. Extensive experiments with three recently released public benchmark databases demonstrate the effectiveness of the proposed methodologies, compared against state-of-the-art gait and action recognition methods.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.Computer ScienceBiometric Gait RecognitionAction RecognitionMotion Feature RepresentationKinect 3D SkeletonDynamic Time WarpingLocal Texture Analysismaster thesis10.11575/PRISM/26931