A Real-time Attendance System Using Deep-learning Face Recognition

Attendance check plays an important role in classroom management. Checking attendance by calling names or passing around a sign-in sheet is time-consuming, and especially the latter is open to easy fraud. This paper presents the detailed implementation of a real-time attendance check system based on face recognition and its results. To recognize a student’s face, the system must first take and save a picture of the student as a reference in a database. During the attendance check, the web camera takes face pictures for a student to be recognized, and then the computer automatically detects the face and identifies a student name who most likely matches the pictures, and finally an excel file will be updated for attendance record based on the face recognition results. In the system, a pre-trained Haar Cascade model is used to detect faces from web camera video. A FaceNet, which has been trained by minimizing the triplet loss, is used to generate a 128dimensional encoding for a face image. The similarity between the encodings of two face images determines whether the two face images coming from the same students. The system has been used for a class, and the results are very satisfactory. A survey has been conducted to investigate the pros and cons of the attendance system on college education management.