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BANK SAFE SECURITY SYSTEM USING MACHINE LEARNING WITH FACE AND LIVE DETECTION
BANK SAFE SECURITY SYSTEM USING MACHINE LEARNING WITH FACE AND LIVE DETECTION
Mr.Aditya Todkar1
Ms.Pranita Shetty2
Ms.Ayesha Shaikh3
Ms.Mukta Tawale4
Students, Department of Computer Engineering
Prof. Ganesh Wayal5
HOD, Department of Computer Engineering
Padmabhooshan Vasantdada Patil Institute of Technology, Bavdhan SPPU Pune
Abstract
Ensuring the security of transactions is currently one of the biggest challenges facing banking systems. The use of biometric authentication of users attracts huge sums money from banks around the world thanks to their convenience and acceptance. Especially in an offline environment where face images from ID documents are compared to digital ones selfie. The selfie vs. id comparison has actually been used in some broader areas as well programs today, such as automatic immigration screening. The great difficulty of such the process is to limit the differences between the comparison face images given their different origins. We propose a new architecture for the cross-domain matching problem based on deep features extracted by two well-referenced convolutional neurons Network (CNN). The results obtained from the collected data, called Face Bank, with more accuracy of more than 93%, indicate the power of the proposed head-to-head comparison problem and its integration into real banking security systems.
Index Terms
Convolutional Neural Networks (CNN), Face Bank, Automatic Immigration control, Digital selfie, Face-to-face comparison problem.
