Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713466
Title: Gait recognition based on 2D and 3D imaging features
Author: Zulcaffle, Tengku Mohd Afendi
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2016
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Abstract:
This thesis focuses on person identification using gait features. The gait features applied in this thesis are acquired from both 2D RGB and 3D Time of Flight (ToF) camera systems. The research has three main parts: (i) lateral view gait period estimation using a single RGB camera system; (ii) the development of a foundational research framework and novel features for frontal view gait recognition using a ToF camera system; and (iii) the development of a novel classification method using the proposed 3D depth features. In the lateral view gait period estimation algorithm, a new gait cycle feature and minimum and maximum point detection methods are proposed. From the experimental results, the proposed method outperforms the previous features and methods in the literature. The second part of the research deals with the development of a novel framework for frontal view gait recognition using a 3D ToF camera. The 3D framework involves: the development of a new dataset of 3D gait image sequence acquired from a frontal view ToF camera system; a new human silhouette extraction algorithm; a frames selection method based on a new gait cycle detection algorithm; and eight gait depth image representations. Overall, the experimental results show that the proposed gait depth image representations produce better results than the previous methods. In the third part of the research, a novel classification method is proposed based on the above gait depth image representations. All the proposed classification method enhances the novel gait depth image representations and outperforms its counterparts. It can be concluded that the proposed method based on the depth information acquired from the Time of Flight camera is suitable for the short period of time
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.713466  DOI: Not available
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