Published Paper Details:

A person's face represents their identity. The development of image processing tools has resulted in a significant shift in the approaches used to exploit this physical property. Every school, college, and library takes attendance. The professor calls on each student by name and logs their attendance in the traditional manner. The process of recording attendance takes time. This is a time-wasting exercise for every lecture. We're going to employ an automatic procedure based on image processing to prevent these losses. One of these pieces of software allows for the identification of individuals within organizations or colleges in order to track attendance. One major problem with computer-based communication is authentication. One significant area of biometric verification that has been applied extensively is human face recognition. The regular tasks of attendance marking and analysis are carried out with less human interaction when an attendance management system is used. We are utilizing a face detection and identification technology in this new method. Accurate attendance depends on face identification, which separates faces from non-faces. Facing recognition for recording the student's attendance is the other tactic. For this system, OpenCV has been utilized. The OpenCV module, which connects to the camera, transforms the picture into RGB format. This format is then translated to neural networks that have already been trained using the HOG technique to identify face pixels. This system provides a time-efficient, efficient method of managing the attendance system with vast scalability for future needs. The gadget automatically detects attendance, the histogram data is compared to an existing dataset, a Haarcascade classifier is used to detect faces, and the Local Binary Pattern Histogram (LBPH) Algorithm is used to distinguish faces in the image. Every hour, an Excel spreadsheet is generated with information from the relevant class instructor.