Securing Atm Transaction With Facial Recognition -based On Verification System

Abstract

ATM Fraud And Unauthorized Transactions Pose Significant Security Challenges In The Banking Sector. Traditional Authentication Methods Such As PINs And Cards Are Vulnerable To Theft, Skimming, And Phishing Attacks. This Paper Proposes An Innovative Solution That Integrates Facial Recognition Technology With ATM Transactions To Enhance Security And User Convenience. By Leveraging Advanced Artificial Intelligence, Specifically Convolutional Neural Networks (CNNs) For Facial Verification, The System Ensures That Only Authorized Users Can Access Their Accounts. The Proposed Solution Captures Real-time Facial Images, Compares Them With Pre-registered Biometric Data, And Grants Transaction Access Only Upon Successful Verification. This Approach Not Only Mitigates The Risks Associated With Stolen Credentials But Also Provides A Seamless And Contactless Authentication Experience. Experimental Results Demonstrate High Accuracy And Robustness Under Varying Lighting Conditions And Facial Expressions. The System’s Real-time Processing Capability Ensures Quick And Secure Transactions, Making It A Practical And Reliable Alternative To Traditional Methods. By Combining Security And Usability, This Solution Addresses Critical Gaps In ATM Transaction Safety, Offering A Scalable And Future-proof Framework For Financial Institutions.

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