A real-time attendance system using deep learning face recognition abstract: 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 a 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 128-dimensional 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. Novel techniques, including multiple-recognition and distance threshold optimization, have been developed to improve the recognition accuracy. The system has been deployed for several classes at our university (no name provided for blind review requirement). The system can be easily tailored for a different application such as access authentication.
Kuang, Weidong and Baul, Abhijit, "A Real-time Attendance System Using Deep-learning Face Recognition" (2020). Electrical and Computer Engineering Faculty Publications and Presentations. 11.
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