The attendance check system is becoming a more challenging task in the real-time system. The process of checking the attendance of the candidates in huge halls is difficult, as they can contain large numbers of attendees. Many attendance management systems have been applied to the topic. However, the traditional attendance management systems still have various issues which motivate researchers to improve the attendance management systems. This paper presents the detailed implementation and application of the new attendance management system in academic libraries through the use of deep learning face-recognition technology and computer vision to address the drawbacks of traditional attendance check methods. The main idea of the system relies on a well-experienced module through the use of machine learning, a pre-trained model, and a database that contributes to the system’s ability to identify the attendees and log in their names, identifications, dates, and times. The study relied on an experimental approach to help determine the extent of the ability of the proposed system to register beneficiaries’ entry into the library efficiently and accurately without any problems occurring. The findings and experimental results show that the proposed system is accurate, fast, reliable and able to recognize up to four faces simultaneously without any technical issues. From the results of the proposed system's accuracy test, it was found that the accuracy of attendance checks when recognizing only one face of a beneficiary was 100%. The accuracy of the attendance checks when recognizing the beneficiary with or without a cap on the head was 100%. The accuracy of attendance checks when recognizing two faces at one time was 100%. The accuracy of attendance checks when recognizing four faces together at one time was 100%. The accuracy of the attendance checks when the beneficiary is facing forward was 100%. The attendance checks' accuracy when the beneficiary faces sideways is 95%. The accuracy of the attendance checks when the beneficiary is facing down is 100%, and when the beneficiary is facing up is 100%. Furthermore, the proposed system does not necessitate any expensive settings, the matter which makes it an appropriate choice for various educational institutions. The study suggests further future research to improve the efficiency of the proposed system, in addition to work on integrating the proposed access system with the automated system applied in the library, which contains a complete database of students and faculty members. This will contribute to producing reports through which various statistics can be obtained to analyze the library’s performance, and to know the reasons for users’ visits and the books they frequently read. |
