Generalization and Efficiency on Finger Print Presentation Attack Anomaly Detection

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.