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Gait Recognition for Automated Visual Surveillance and Security

ABSTRACT

The security of high-privileged premises like self-service bank and company safety vault is very crucial. In reality, many robberies and violent events have occurred at these premises and resulted in great financial losses and physical injuries. If these events can be instantly detected and controlled, the resulting damages could be reduced. However, as the majority of the present video surveillance systems cannot automatically detect these events, they are not able to stop the violent incidents or reduce the damages incurred. In this respect, gait recognition appears as an attractive solution to this problem. Gait recognition is used to signify the identity of a person based on the way the person walks. This is an interesting property to recognize a person, especially in surveillance or forensic applications where other bio-metrics may be inoperable. For example in a bank robbery, it is not possible to obtain face or fingerprint impressions when masks or hand gloves are worn. Therefore, gait appears as an attractive solution because gait is discernible even from a great distance.

Status : Completed
RESEARCHERS
Dr. Tee Connie
Dr. Goh Kah Ong Michael
COLLABORATOR
Andrew Beng Jin Teoh, School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea
PUBLICATIONS
  • Tee Connie, Andrew Beng Jin Teoh, Michael Goh, “Human gait recognition using localized Grassmann mean representatives with partial least squares regression”, Accepted by Multimedia Tools and Application, 2018. (ISSN: 14321882, 13807501)
  • Tee Connie, Michael Goh, Andrew Teoh, “Multi-view Gait Recognition using a Doubly- Kernel Approach on the Grassmann Manifold”, Neurocomputing, Springer, Vol. 216, pp. 534-542, 2016. (ISSN: 0925-2312)
  • Tee Connie, Michael Goh, Andrew Teoh, “A Grassmannian Approach to Address View Change Problem in Gait Recognition”, IEEE Transactions on Cybernetics, Vol. 47, Issue 6, pp. 1395-1408, 2016. (ISSN: 2168-2267)
  • Tee Connie, Michael Goh, Andrew Teoh, “A Grassmann graph embedding framework for gait analysis”, EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2014. (ISSN: 1687-6180)