Model Based Gait Recognition Using Skeletal Data

Hasan, Md. Bakhtiar; Amin, Naeer Jawad

dc.contributor.author	Hasan, Md. Bakhtiar
dc.contributor.author	Amin, Naeer Jawad
dc.date.accessioned	2020-10-27T14:22:51Z
dc.date.available	2020-10-27T14:22:51Z
dc.date.issued	2018-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/581
dc.description	Supervised by Prof. Dr. Md. Hasanul Kabir	en_US
dc.description.abstract	Being the very first in the category of low-cost consumer-level depth sensors, Microsoft Kinect has opened the door to a new generation of computer vi- sion and biometric security applications after its release. This thesis focuses on designing new methodologies for Kinect-based gait recognition systems that utilize the Kinect 3D virtual skeleton to construct effective and robust mo- tion representations. Our goal is to propose a gait recognition method that focuses on designing a feature descriptor that can capture person-specific dis- tinct motion patterns, caused by the influence of human physiology and be- havioral traits. In this regard we use pre-existing representations of skeletal data namely Joint Relative Distance and Joint Relative Angle. The proposed methodologies contain more representations using mean and standard devi- ation of the data which can effectively handle view and pose variations. We used a dynamic time warping-based kernel that takes a collection of sequences as parameters and computes a dissimilarity measure between the training and the unknown sample. It can effectively handle variable walking speed without any need of extra pre-processing. The effectiveness of the proposed method- ologies are evaluated using 3D skeletal gait database captured with a Kinect v1 sensor. In our experiments, fusion of mean and standard deviation achieves promising results, as compared against the previous implementations.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh	en_US
dc.title	Model Based Gait Recognition Using Skeletal Data	en_US
dc.type	Thesis	en_US