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Deep Learning for Motion Recognition

Daraei, Sara (2021) Deep Learning for Motion Recognition. Doctoral Dissertation, University of Pittsburgh. (Submitted)

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Automatic analysis and interpretation of human motion from visual data has been one of the most significant computer vision challenges since 1970. In recent years, deep learning has fueled the rapid advancement of computer vision topics. In particular, human motion analysis has drawn substantial attention due to its practical importance in many applications in a variety of domain including social behavior studies, medical assistance, robotics, sport analytics, and more.
Human motion is one of the key parts of human social behavior and a rich source of information. We move our whole body involving head, shoulders, hands, trunk, legs, and limbs combined with facial expressions flavored with our individualized style to transmit social signals. A number of studies have suggested the existence of unique motion signatures of individuals by analyzing data obtained from KinectTM devices, and Electromyography (EMG) electrodes attached to muscles. Meaning that when we move and communicate, we tend to use our characteristic style of motion. These distinct motion patterns are attributed to behavioral and anatomical di↵erences between individuals as well as their di↵erent muscle activation strategies.
This research aims at establishing a fully-automated framework to push the envelope of understanding information hidden in human motions from visual inputs and its potential applications on a set of fundamental tasks including classification, identification, and user authentication. For this purpose, we propose a number of deep learning approaches and try to tackle the problem from a data-driven perspective and figure out to what extend we would be able to model human motion signatures and see if it is possible to authenticate or identify people based on their movement pattern. Our results demonstrate an accuracy of 94.04% for human authentication and 92.62% for human identification among 10 subjects confirming that human motion conveys information regarding their identity and can be considered as practical biometric cues. Considering particular applications and their limitations, we further
propose a generative biometric model that efficiently learns task-relevant features in data and integrate them into a probabilistic authentication setting based on limited amount of data. The proposed framework is able to authenticate the correct subject 86.11% of times.


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Item Type: University of Pittsburgh ETD
Status: Submitted
CreatorsEmailPitt UsernameORCID
Daraei, Sarasad133@pitt.edusad133
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Munro, Paulpwm@pitt.eduPWM
Karimi, Hassanhkarimi@pitt.eduHKARIMI0000-0001-5331-5004
Lewis, Michaelcmlewis@pitt.educmlewis0000-0002-1013-9482
Abdelhakim, MMAIA@pitt.eduMAIA0000-0001-8442-0974
Date: 10 November 2021
Date Type: Submission
Defense Date: 5 November 2021
Approval Date: 17 January 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 123
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Human Authentication, Human Identification, Biometrics Recognition, Feature Learning, Recurrent Neural Networks
Date Deposited: 17 Jan 2022 15:02
Last Modified: 17 Jan 2022 15:02


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