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

\bibitem{asee_peer_33949}
  Kuang, W., \& Baul, A. (2020, June), \emph{ 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

Weidong Kuang and Abhijit Baul.  " A Real-time Attendance System Using Deep-learning Face Recognition".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/33949 Internet.  10 Jan, 2025

\bibitem{asee_peer_33949}
  Weidong Kuang and Abhijit Baul.
  " A Real-time Attendance System Using Deep-learning Face Recognition".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/33949 Internet.  10 Jan, 2025

@INPROCEEDINGS{asee_peer_33949
author = "Weidong Kuang and Abhijit Baul"
title = " A Real-time Attendance System Using Deep-learning Face Recognition"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/33949}
number = {10.18260/1-2--33949}
}

TY  - CPAPER
AB  - 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.  
AU  - Weidong Kuang
AU  - Abhijit Baul
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  -  A Real-time Attendance System Using Deep-learning Face Recognition
UR  - https://peer.asee.org/33949
DO  - 10.18260/1-2--33949
ER  -