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Presentation Attack Detection Framework
Book chapter   Peer reviewed

Presentation Attack Detection Framework

Hossain Shahriar and Laeticia Etienne
Machine Intelligence and Big Data Analytics for Cybersecurity Applications, pp.297-311
Studies in Computational Intelligence, Springer International Publishing
2021
DOI: https://doi.org/10.1007/978-3-030-57024-8_13

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Abstract

Haar cascade classifier Liveness detection Local binary pattern Presentation attack Cybersecurity
Biometric-based authentication systems are becoming the preferred choice to replace password-based authentication systems. Among several variations of biometrics (e.g., face, eye, fingerprint), iris-based authentication is commonly used in every day applications. In iris-based authentication systems, iris images from legitimate users are captured and certain features are extracted to be used for matching during the authentication process. Literature works suggest that iris-based authentication systems can be subject to presentation attacks where an attacker obtains printed copy of the victim’s eye image and displays it in front of an authentication system to gain unauthorized access. Such attacks can be performed by displaying static eye images on mobile devices or iPad (known as screen attacks). As iris features are not changed, once an iris feature is compromised, it is hard to avoid this type of attack. Existing approaches relying on static features of the iris are not suitable to prevent presentation attacks. Feature from live Iris (or liveness detection) is a promising approach. Further, additional layer of security from iris feature can enable hardening the security of authentication system that existing works do not address. To address these limitations, this chapter introduces iris signature generation based on the area between the pupil and the cornea. Our approach relies on capturing iris images using near infrared light. We train two classifiers to capture the area between the pupil and the cornea. The image of iris is then stored in the database. This approach generates a QR code from the iris. The code acts as a password (additional layer of security) and a user is required to provide it during authentication. The approach has been tested using samples obtained from publicly available iris database. The initial results show that the proposed approach has lower false positive and false negative rates.

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