Rapid development of IT has brought a change in the traditional attendance system. The Automatic Attendance System (AAS) identifies students to take attendance, and saves its result in the database to generate necessary information such as various statistics.
To identify students, it is necessary to read the information of physical means such as RFID (Radio Frequency Identification) card, or the biometric information such as their fingerprints or faces.
AAS based on RFID needs all students to carry their cards and involves manual operation when they forget to bring or lose it. In the meantime, the AAS based on fingerprints is fast and simple, and it needs no physical RFID cards. However, fingerprint readers must be installed at every lecture room and it takes long to recognize students by their fingerprints one by one.
Therefore, the AAS based on face recognition are accepted to be efficient as cameras are installed in most schools.
Recently, great progress in the field of face recognition has led to new applications of this technology, and the development of various products has been brisk. In a paper, they presented a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where face similarities are represented as distances. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors. This approach has great application effect as it shows great face recognition performance for face images of only 128 bytes.
The multi-face recognition system detects and recognizes several faces from an image, which requires high-resolution cameras. However, it is too expensive for some schools, and it is impossible to recognize all faces in a large classroom such as a practical training room because it usually has a view angle of 40 degrees. Of course, a high-resolution camera with a wide angle lens is a solution, but it is more practical to utilize the existing cameras than purchasing expensive new ones.
Kim Myong Jin, a lecturer at the Faculty of Information Science and Technology, has implemented multi-face recognition by combining several cameras and proposed an AAS based on it. This is a system that recognizes students’ faces from the images captured by existing different types of cameras, and automatically registers their attendance. For face recognition, FaceNet which shows high performance is used.
This system enables automatic register of attendance by means of low-resolution cameras even in large classrooms. The experiments have proved that the proposed system guarantees the accuracy of 100% in well-lit conditions with suitable allocation of cameras.
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