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Anti-tailgating solution using biometric authentication, motion sensors and image recognition

  • University of Bedfordshire
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

Tailgating is a social engineering attack challenging physical security within organizations. It gained public traction in the year 1999 and has since remained a major concern in the field of security leading to the development of several anti-tailgating solutions. These solutions began with simple mechanisms like mechanical turnstiles, revolving doors, and man-trap systems and evolved into more modern technologies using infrared beams, 3D machine vision, face detection, BMI and face recognition combination, and an embedded solution using IP camera and video analytics. A critical analysis of these solutions uncovered certain weaknesses which run through most of them. These are the inability to detect two people side by side and the incapability of detecting multiple entries after a single access authorization. These shortfalls led to the development of the solution in this paper which aims to eliminate the shortcomings of existing technologies and boost security, by using a three-step anti-tailgating solution. The design science research methodology and aspects of qualitative and quantitative research are employed in designing a three-step anti-tailgating solution that combines face detection, palm recognition, and motion sensors, to eliminate the loopholes of existing technologies. The results from experimentation indicated that the face detection tool could detect two faces present. The motion sensors were shown to be efficient in performing people counting and detection, to eliminate tailgating and discrepancies in the number of entries against the number of authorized personnel. Integrated with palm recognition the overall system will function effectively because the three technologies complement each other's shortfalls, therefore preventing tailgating. It is concluded that this system will be an improved and more effective anti-tailgating solution.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 825-830 (6 pages)

Publication milestones

  • Published - 15/03/2022

Publication status

Published - 15/03/2022

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

Publication series

  • Publication series name: Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

ISBN (Print)

9781665421744

ISBN (Electronic)

9781665421744

External Publication IDs

  • handle.net: 10547/626161
  • Scopus: 85127572770

Host publication title

Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

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