Template-Based Gait Authentication Through Bayesian Thresholding

Ebenezer R. H. P. Isaac; Susan Elias; Srinivasan Rajagopalan; K.S. Easwarakumar

doi:10.1109/JAS.2019.1911345

Volume 6 Issue 1

Jan. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(1): 209-219

Ebenezer R. H. P. Isaac, Susan Elias, Srinivasan Rajagopalan and K.S. Easwarakumar, "Template-Based Gait Authentication Through Bayesian Thresholding," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 209-219, Jan. 2019. doi: 10.1109/JAS.2019.1911345

Citation:

Ebenezer R. H. P. Isaac, Susan Elias, Srinivasan Rajagopalan and K.S. Easwarakumar, "Template-Based Gait Authentication Through Bayesian Thresholding," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 209-219, Jan. 2019. doi: 10.1109/JAS.2019.1911345

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Template-Based Gait Authentication Through Bayesian Thresholding

doi: 10.1109/JAS.2019.1911345

1.
Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600036, India
2.
School of Electronics Engineering, VIT University, Chennai 600048, India
3.
Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905 USA
4.
Department of Computer Science and Engineering, Anna University, Chennai 600025, India

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Author Bio:
Ebenezer R. H. P. Isaac (M'14) received the B. E. and M.E. degrees in the Department of Computer Science and Engineering from Anna University, Chennai and completed his Ph.D. degree in the Department of Computer Science and Engineering, Anna University, Chennai, CEG Campus.
He is currently a Postdoctoral Research Fellow in the Department of Computer Science and Engineering, Indian Institute of Technology Madras. He is a member of both ACM and IEEE. His research interests include machine learning, biometrics, gait analysis, and data science. (email: ebeisaac@ieee.org)

Susan Elias received M.E. degree in multimedia technology and Ph.D. degree in computer science from Anna University, Chennai, CEG Campus. She was subsequently selected for a Post-Doctoral Fellowship at the Department of Computer Science and Engineering, Indian Institute of Technology, Madras. She is currently an Associate Professor and the Program Chair for the Electronics and Computer Engineering Department at VIT University Chennai Campus.
She was earlier in the Department of Computer Science at Sri Venkateswara College of Engineering (SVCE) for almost two decades. She was the Head of the Computer Science Department and the Dean for Co-curricular activities at SVCE. She had been the Principal Investigator for two projects funded by (Defence Research Development Organisation (DRDO) Govt. of India. Her research interests include machine learning and design of smart environments for solving socially relevant problems. She is also actively involved in designing bio-medical products for the benefit of the society. (email: susan.elias@vit.ac.in)

Srinivasan Rajagopalan received the Ph.D. degree from Mayo Clinic College of Medicine. He has published more than 200 peer reviewed journal and conference publications, and filed more than 25 international patents of which 10 have already been issued. He has also made pioneering contributions in image-guided neurosurgery that is currently integrated into major image guidance systems. As an active member of IEEE, MICCAI, Society for Biomaterials, and SPIE, he has won numerous awards from these societies. He is the first and only Indian to date to have received the prestigious young investigator award conferred by the MICCAI society. He has also developed over fifteen image understanding applications that are routinely used in clinical care worldwide.
He currently heads a multi-disciplinary team of scientists, engineers, and clinicians to develop unique clinical frameworks for automatic understanding of medical images. His techniques are routinely applied in the clinical assessment of lung diseases at Mayo Clinic.
Prior to joining Mayo Clinic, he has worked for IBM Singapore, Center for Information Enhanced Medicine, and General Electric Global research. He has successfully deployed his technical skills to further the practice of conventional and interventional radiology, cardiology, neurology, pulmonology, ophthalmology, and tissue engineering. His technical contributions spans across parallel computing, high speed imaging, image based metrology, scientific visualization, geometric modeling, multi-dimensional image analysis, fuzzy logic, validation methodologies, and pattern recognition.(email: rajagopalan.srinivasan@mayo.edu)

K. S. Easwarakumar received the M.Tech degree in computer and information sciences from Cochin University of Science and Technology, Cochin and PhD in Computer Science and Engineering from Indian Institute of Technology, Madras.
He was the Head of the Department of Computer Science, in Anna University, Chennai for three years. For the past 25 years, he has dedicated himself to academia in computer science. His research interests include parallel and distributed computing, data structures, graph algorithms, parallel algorithms, computational geometry, theoretical computer science, molecular computing, and pattern recognition.(email: easwarakumarks@gmail.com)
Corresponding author: Ebenezer R. H. P. Isaac, email: ebeisaac@ieee.org
Received Date: 2018-05-12
Accepted Date: 2018-06-20

Abstract

Abstract

While gait recognition is the mapping of a gait sequence to an identity known to the system, gait authentication refers to the problem of identifying whether a given gait sequence belongs to the claimed identity. A typical gait authentication system starts with a feature representation such as a gait template, then proceeds to extract its features, and a transformation is ultimately applied to obtain a discriminant feature set. Almost every authentication approach in literature favours the use of Euclidean distance as a threshold to mark the boundary between a legitimate subject and an impostor. This article proposes a method that uses the posterior probability of a Bayes' classifier in place of the Euclidean distance. The proposed framework is applied to template-based gait feature representations and is evaluated using the standard CASIA-B gait database. Our study experimentally demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclidean distance approach and the cosine distance which is established in research to be the current state of the art.
- Bayes' rule,
- biometrics,
- gait recognition,
- linear discriminant analysis,
- verification

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References(30)

References

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Template-Based Gait Authentication Through Bayesian Thresholding

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