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This research project aims to enhance the authentication process of identification cards within college campuses using advanced image recognition technologies, specifically Convolutional Neural Networks (CNN) and OpenCV methodologies.By integrating sophisticated object detection techniques such as Viola Jones, LBPH (Local Binary Pattern Histogram), and YOLO (You Only Look Once), along with advanced image segmentation methods, the system endeavors to accurately identify and verify individuals who comply with ID card protocols.Through extensive training on diverse datasets containing images of students both with and without ID cards, the system dynamically learns to recognize and differentiate between compliant and non-compliant scenarios.The adoption of this technology promises significant advantages, primarily in automating attendance tracking and reinforcing security measures within educational environments.By automating the attendance recording process and strengthening security protocols, the system ensures that only authorized individuals wearing valid identification cards are granted access to campus facilities.The system's seamless architecture encompasses various phases, including enrolling individual details, capturing and analyzing images, validating ID card adherence through segmentation-assisted recognition, logging attendance upon validation, and documenting violations for unauthorized entry attempts.Overall, this comprehensive framework enhances operational efficiency while creating a secure and conducive learning environment within educational institutions.
- et al. (Mon,) studied this question.