Advanced International Journal of Multidisciplinary Research

E-ISSN: 2584-0487 •   Impact Factor: 9.11

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Deepest Learning-based Hybrid Facial Recognition for Improved Drivers License Authentication

Author(s) Kadiyala Ramu, Sane Mounika
Country India
Abstract Facial recognition technology is used to provide a safe and dependable driver's license verification system. Variations in lighting conditions, facial alignment, image noise, partial occlusion, and low-quality photographs can all cause problems for conventional facial recognition techniques and reduce their detection accuracy. Strong defense against spoofing and illegal access is also necessary for real-time authentication. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are combined in a hybrid strategy to improve recognition accuracy and authentication dependability in order to overcome these issues. Accurate face detection and feature representation are made possible by the CNN model's extraction of important facial traits and spatial data from images. In order to distinguish real users from spoofing attempts, such as printed images or replayed films, the LSTM network examines sequential patterns. The system obtains pertinent data, such as the user's name, license number, date of birth, and address, by securely comparing the verified face with stored licensing records. In real-world settings, the method improves identity verification, lowers unauthorized access, and boosts overall system performance.
Keywords Facial recognition, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Deep Learning, Driver’s license.
Discipline Engineering
Published In Volume 4, Issue 3, May-June 2026
Published On 2026-06-04
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