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Deep Learning - Powered Hybrid Facial Recognition for Enhanced Drivers License Authentication

Author(s) Ms. M. Vijaya Sree, B. Prathyusha, C. Swetha, M Sai Ganesh, B.V. Haripratap Reddy
Country India
Abstract Facial recognition systems used to extract and verify the driver’s license information are fraught with numerous technical issues such as changes in lighting, face orientation, occupancy, image obscuration, and image noise, not to mention that real time processing requires security. It is a complex activity to develop an efficient system that can properly recognize a person utilizing low-quality or distorted images and avoid cases of spoofing. This study suggests a hybrid model, which is composed of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance the accuracy of detection and the reliability of authentication. CNN part is used to obtain discriminative spatial information of a facial image so that it can be used to detect facial features and represent features accurately. This LSTM network takes sequential patterns and verifies licenses effectively separating actual users and spoofing techniques like the use of static image or re-played video. Upon authentication of the face, the system conducts a secure comparison against the stored records and retrieves the appropriate information that is associated with the license such as the name, license number, date of birth and address. The suggested approach will increase identity checks, minimize the chances of fraud access, and optimize the overall system performance in the case of real-life conditions.
Keywords : Facial recognition, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Deep Learning, Driver’s license.
Field Engineering
Published In Volume 7, Issue 4, April 2026
Published On 2026-04-04
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