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

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2020 ASEE Virtual Annual Conference Content Access


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Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computing and Information Technology Division Technical Session 8

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Computing and Information Technology

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Paper Authors


Weidong Kuang University of Texas Rio Grande Valley

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Dr. Weidong Kuang received a Ph.D degree in Electrical Engineering at University of Central Florida in 2003. He has be with University of Texas Rio Grande Valley since 2004 starting as an assistant professor, up to an associate professor now. His research interests include VLSI design, machine learning, and digital signal processing.

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Abhijit Baul University of Texas Rio Grande Valley

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I am a Master's student in the University of Texas Rio Grande Valley. I have completed my Bachelor of Science in Electrical Engineering from Bangladesh University of Engineering and Technology. My research interests are deep learning and computer vision.

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title: 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, W., & Baul, A. (2020, June), A Real-time Attendance System Using Deep-learning Face Recognition Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--33949

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