Fake Biometric Detection for Face and Fingerprint
Gandhapu Yashwanth1, Gokavarapu Manikanta Kalyan2, Singamsetty Phanindra3, M. Jasmine Pemeena Priyadarsini4

1Gandhapu Yashwanth, School of Electronics Engineering, VIT University, Vellore, India.
2Gokavarapu Manikanta Kalyan, School of Electronics Engineering, VIT University, Vellore, India.
3Singamsetty Phanindra, School of Electronics Engineering, VIT University, Vellore, India.
4M. Jasmine Pemeena Priyadarsini, School of Electronics Engineering, VIT University, Vellore, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 21, 2020. | Manuscript published on June 10, 2020. | PP: 589-595 | Volume-9 Issue-8, June 2020. | Retrieval Number: H6462069820/2020©BEIESP | DOI: 10.35940/ijitee.H6462.069820
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: Active Shape Models(ASM), Local Binary Patterns(LBP), Pyramid Histogram of Oriented Gradients(PHOG), Random Forest(RF), Speeded-Up Robust Features(SURF).
Scope of the Article: Pattern Recognition