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Adaptive Fingerprint Verification Using Siamese Neural Networks and Transfer Learning for Robust Authentication of Damaged Prints | ||
| Journal of Information Technology Management | ||
| دوره 18، شماره 1، 2026، صفحه 1-16 اصل مقاله (1.66 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2026.106251 | ||
| نویسندگان | ||
| Suman M* 1؛ Shobha N2؛ Sridevi Muruhan3؛ Vinothkumar S4؛ Ramkumar R5؛ R J Poovaraghan6 | ||
| 1Assistant Professor, Department of Artificial Intelligence and Data Science, B.M.S College of Engineering, Affiliated to Visvesvaraya Technological University, Belagavi, India. | ||
| 2Associate Professor, Department of Computer Science and Design, Dayananda Sagar College of Engineering, Bangalore, Visvesvaraya Technological University, Belagavi, India. | ||
| 3Professor, Department of Biotechnology, Vinayaka Mission`s Kirupananda Variyar Engineering College, Salem (Vinayaka Mission`s Research Foundation), India. | ||
| 4Assistant Professor, (SRG), Department of Information Technology, Kongu Engineering College, Perundurai, Erode, India. | ||
| 5Associate Professor, Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam. | ||
| 6Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India. | ||
| چکیده | ||
| Fingerprint recognition is a vital component of biometric authentication, yet its reliability often declines with damaged or partial inputs. This study introduces a fingerprint verification framework based on Siamese Neural Networks (SNN) with transfer learning to enhance adaptability and accuracy. Using the Sokoto Coventry Fingerprint Dataset (SOCOFing), fingerprint images were preprocessed into labeled pairs of similar and dissimilar samples for model training and evaluation. A distinctive feature of the proposed system is dynamic class expansion, enabling new user integration without retraining. The framework also ensures privacy-preserving verification by securely encrypting and storing extracted feature embeddings. Experimental results across four difficulty levels—real, easy, medium, and hard—demonstrate high reliability and robustness in both intact and degraded fingerprint conditions. Overall, the proposed approach offers a scalable, secure, and efficient solution for fingerprint-based identification, addressing key challenges in real-world biometric authentication systems. | ||
| کلیدواژهها | ||
| Fingerprint Authentication؛ ConvNeXt Feature Extraction؛ Siamese Neural Network (SNN)؛ AES Encryption؛ Biometric Security | ||
| مراجع | ||
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