Open AccessFingerprint Distortion Detection
Author(s) -
Harshada Kanade,
Gauri Uttarwar,
Shweta Borse,
K. S. Archana
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2063204
Subject(s) - biometrics , computer science , fingerprint (computing) , identification (biology) , authentication (law) , artificial intelligence , fingerprint recognition , key (lock) , distortion (music) , pattern recognition (psychology) , cryptography , identity (music) , data mining , computer vision , machine learning , computer security , amplifier , computer network , botany , physics , bandwidth (computing) , acoustics , biology
Fingerprint is widely used in biometrics, for identification of individual’s identity. Biometric recognition is a leading technology for identification and security systems. It has unique identification among all other biometric modalities. Most anomaly detection systems rely upon machine learning. Calculations are performed to identify suspicious occasion. The primary purpose of this system is to ensure a reliable and accurate user authentication; this study addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. The approach is to utilize local patches centered and aligned using fingerprint details. That proposed approach is to provide accuracies in fingerprint spoof detection for intra-sensor, cross material, crosssensor, as well as cross-dataset testing scenarios. The principle used is similar to the working of some cryptographic primitives, in particular to present the key into the plan so that a couple of operations are infeasible without knowing it.