A Hybrid Facial Recognition System for Secure Driver's License Verification Using Deep Learning

Extracting driver's license information using facial recognition presents significant challenges, including handling diverse lighting conditions, face orientations, and occlusions while ensuring data security. Designing and implementing driver's license information using facial recognition systems that can handle poor image quality, excessive noise, and identification in real-time is difficult. In order to solve these problems, this study introduce a novel approach which is the hybridization of Eeigen faces algorithm and deepface algorithm ensures to deliver efficient accurate face detection as liveness would be ensured from the MobileNet perspective to rule out a true face from being photographically obtained or spoofed. The system is integrated with Firebase to store and retrieve license details like name, license number, date of birth, and address. Upon detection and verification of face, the system matches this face to the corresponding record available in the Firebase database and displays license information. This approach ensures access to the information provided, minimizing fraud and enhancing authentication accuracy.