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Enhancement of Energy Efficiency in Wireless Sensor Network with Mobile Sink: A Survey Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Facial Landmark Features-Based Face Misclassification Detection System

Authors : Aditya Bakshi, Sunanda Gupta

Published in: Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Publisher: Springer Nature Singapore

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Abstract

Issues of face spoofing that can evade the verification system by placing the photo of real user on camera have been discussed a lot in the literature survey. By detecting the person through misclassification, the problem could be minimized. Therefore, in this paper, robust face misclassification detection system is proposed using ABT mechanism. The proposed system provides the additional level of security before face recognition module. Face landmark features such as eye, nose, and mouth movements are used for generating challenges for detecting fake users from genuine users using misclassification. The reliability of system is tested by placing photographs and videos from Replay-Attack database and live database. Proposed system gives good results under spoofing attacks such as eye imposter attack and mouth imposter attack. The results show that system detects the fake user when implemented on all types of attacks and confirms the 79.6% misclassification detection.

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Metadata
Title
Facial Landmark Features-Based Face Misclassification Detection System
Authors
Aditya Bakshi
Sunanda Gupta
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Print ISBN: 978-981-19-1141-5

Electronic ISBN: 978-981-19-1142-2

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-1142-2_27