Advanced International Journal for Research

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Fingerprint Based ATM System(Biobank-Secure)

Author(s) Ms. Pranali Ravindra Bagul, Ms. Harshada Gulab Panchal, Ms. Gayatri Sachin Waghmare, Ms. Shubhangi Sidheshwar Waghchaure, Prof. Yashraj Chavan
Country India
Abstract The rapid evolution of digital technology has transformed critical sectors such as banking, healthcare, and research, making data security an essential concern. Traditional authentication methods, including passwords and PINs, are increasingly inadequate due to risks such as phishing, brute-force attacks, and insider threats. This research proposes Biobank-Secure, a full-stack web-based biometric authentication system utilizing fingerprint technology to address these vulnerabilities. The system integrates a Java-based backend, a responsive web frontend, and deep learning techniques through Convolutional Neural Networks (CNNs) for feature extraction. Fingerprints, being unique and difficult to forge, provide a reliable mechanism for user verification. The backend employs RESTful APIs to facilitate secure communication between the client and server. Extensive testing demonstrates that Biobank-Secure achieves an authentication accuracy of approximately 92%, with precision and recall metrics ranging from 90% to 94%. The results indicate that deep learning-based fingerprint verification can significantly enhance security, reduce reliance on human intervention, and provide a scalable solution for high-stakes applications such as biobanks, research laboratories, and banking systems. This paper further discusses the system’s three-tier architecture, the technological stack, and potential avenues for future improvements, including integration with IoT-based environmental context and multi-modal biometric fusion.
Keywords Biometric Authentication, Fingerprint Recognition, Deep Learning, Convolutional Neural Networks (CNN), Cybersecurity.
Field Biology
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-03-05
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E-ISSN 3048-7641
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