Library Attendance System Using Yolov5 Faces Recognition in Deep Learning

Abstract

The manual technique of managing attendance takes a lot of time and effort to keep up. Hence, the procedure for managing students' attendance in schools and universities ought to be computerized. Attendance may be tracked using a variety of biometric techniques. Facial recognition technology is widely employed. Without human intervention, attendance will be recorded alongside the suggested work. This approach fixes the camera within the classroom. From the collected image, faces are located and then identified using a trained model. The class teacher then verifies and marks the attendance. The absences are noted in the attendance and reported to the parents.When identifying many faces at once, algorithmic and computational hurdles occur. Because there are so many interrelated subsystems in a library, It is incredibly difficult to combine a facial recognition system and an existing automation system. A library attendance system prototype is being developed to aid library management with the facial identification of library visitors. The YOLOv5 algorithm is used in this work to focus on face identification in pictures. The library attendance system integrates the visitor identification system, YOLOv5 face recognition and API service. The findings show that the library attendance system can run quickly, read the API service, and provide results data.