Open AccessFake Biometric Detection for Face and Fingerprint
Author(s) -
Gandhapu Yashwanth*,
Gokavarapu Manikanta Kalyan,
Singamsetty Phanindra,
M. Jasmine Pemeena Priyadarsini
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.h6462.069820
Subject(s) - spoofing attack , biometrics , fingerprint (computing) , face (sociological concept) , artificial intelligence , minutiae , computer science , feature (linguistics) , feature extraction , pattern recognition (psychology) , computer vision , facial recognition system , local binary patterns , crime scene , random forest , fingerprint recognition , face detection , computer security , image (mathematics) , geography , histogram , social science , linguistics , philosophy , archaeology , sociology
Face and Fingerprint acknowledgment is most popular and generally utilized as a biometric innovation as a result of their high ampleness and peculiarity. Besides the recognizing the user the present biometric systems have to face up with the new troubles like the spoofing attacks, like presenting a photo of the person to the camera. We study the anti-spoofing solutions for distinguishing between original and fake ones in both face and fingerprint in this paper. Generally, the face arrangement and portrayal that exhibits enhancements in coordinating execution over the more typical all-encompassing way to deal with face arrangement and depiction. Face detection, introduced in this paper, comprises the accompanying significant advances like facial features locating using Active Shape Models (ASM), Local Binary Pattern for feature extraction which is known for its texture classification, and Random Forest is used for classification. a fingerprint comprises of edges and valleys design otherwise called furrows. For Fingerprint detection, introduced in this paper includes the accompanying significant advances like Minutiae based local patches, SURF, and PHOG for feature extraction, and Random Forest is used for classification. The proposed methodologies are profoundly seriously contrasted and different as the investigation of the general picture nature of real biometric tests uncovers essential data for both face and fingerprints that might be productively used to segregate them from fake attributes.