Biometric User Authentication System via Fingerprints Using Novel Hybrid Optimization Tuned Deep Learning Strategy
Senthil Kumar Natarajan, Rathinasabapathy Ramadevi, Jaisankar Narayanasamy, Arikrishnaperumal Ramaswamy Aravind · Traitement du signal · 2023
Due to security concerns, the necessity for authentication and identity techniques has increased in the modern world.A novel Accurate and Automated Fingerprint Biometric Authentication Model (AAFBAM) is introduced.The operation of the suggested AAFBAM is divided into two parts: (a) enrollment and (b) verification.During the enrollment phase, the database is prepared, and the input fingerprint is authenticated during the identification phase.The enrollment phase includes the data acquisition stage, preprocessing feature extraction stage, and minutiae point detection phase.The minutiae point detection is performed using the MISHO-based Optimized Deep Neural Network (MISHO-DNN) classifier.The weight function of DNN is tuned optimally using the proposed Memory Integrated Spotted Hyena Optimization (MISHO) algorithm to enhance its detection accuracy.The Verification Phase includes the preprocessing, feature extraction stage, minutiae point detection with MISHO-DNN, minutia matching, and minutiae score evaluation.Here, the minutiae score is obtained by matching minutiae from both phases and is compared with the threshold value.When the minutiae score exceeds the threshold, the user is identified as the genuine user, and their request is accepted.Otherwise, the user is recognized as an unauthenticated user, and their request is rejected.Finally, a comparative evaluation is conducted to validate the efficiency of the projected model.