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Fingerprint Spoofing Detection to Improve Customer Security in Mobile Financial Applications Using Deep Learning

  • Shefali AroraEmail author
  • M. P. S Bhatia
Research Article - Computer Engineering and Computer Science
  • 4 Downloads

Abstract

Online banking and financial services using mobile applications are seeing a persistent growth among customers, who are using these for their financial transactions. This rise in the use of such applications in smart devices has increased security concerns. There is need for secure mechanisms to prevent fraud and protect personal information. This paper investigates the use of biometric identification in banking and financial services, which leverage the use of smartphones and tablets. While customer engagement and brand loyalty are important concerns, these services are making use of biometric authentication to make customer interactions more secure. However, as technology is growing rapidly, spoofing attacks are becoming common. In this paper, authors have proposed a robust framework to detect spoofing attacks in fingerprint recognition. The process of spoofing detection involves contrast enhancement using histogram equalization and a deep convolutional neural network architecture. Authors have validated the results on various biometric spoofing benchmarks, each one containing real and spoofed samples of user fingerprints. The results indicate that our proposed framework performs better as evaluated against other existing pre-trained CNN models and state-of-the-art methods.

Keywords

Biometrics Fingerprint spoof detection Convolutional neural networks Smart devices Deep learning Security Banking Financial services Mobile applications Online banking 

Notes

Acknowledgements

We would like to thank Biometric Recognition Group-ATVS, Idiap Research Institute, Biometric System Laboratory, Pattern Recognition and Image Processing Laboratory, Biometric Test Centre and Center for Identification Technology Research (Clarkson University) [58, 59, 60, 61, 62, 63] for providing datasets to evaluate the proposed framework.

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Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Netaji Subhas Institute of TechnologyNew DelhiIndia

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