Generalization and Efficiency on Finger Print Presentation Attack Anomaly Detection

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Author(s): Hemalatha J. (Department of Computer Science and Engineering, AAA College of Engineering and Technology, India), Vivek V. (AAA College of Engineering and Technology, India), Kavitha Devi M. K. (Thiagarajar College of Engineering, India)and Sekar Mohan (AAA College of Engineering and Technology, India)
Copyright: 2023
Pages: 18
Source title: Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era
Source Author(s)/Editor(s): A. Srinivasan (SASTRA University (Deemed), India)
DOI: 10.4018/978-1-7998-8892-5.ch023

Keywords: Artificial Intelligence / Computer Science & IT / Computer Vision & Image Processing / Information Science Reference

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Abstract

Biometric identification systems are highly used for verification and identification like fingerprint recognition, voice recognition, face recognition, etc. The very famous biometric technique is fingerprint recognition. A fingerprint is the pattern of ridges and valleys on the surface of a fingertip. The endpoints and crossing points of ridges are called minutiae. The basic assumption is that the minutiae pattern of every finger is unique and does not change during one's life. In the present era, fingerprint-based biometric authentication system gets popularized, but still, this biometric system is vulnerable to various attacks, particularly presentation attacks. This chapter explains how the knowledge-driven neural networks work on fingerprint anomaly detection. In addition, the various features available to detect the anomaly in biometric are also discussed.

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Generalization and Efficiency on Finger Print Presentation Attack Anomaly Detection

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