Application of AI Face Recognition Technology in Swipe Card Attendance Systems for Hospitals


  • Te-Kwei Wang Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
  • Yu-Hsun Lin Department of Business and Management, Ming Chi University of Technology, New Taipei City, Taiwan
  • Kai-Ping Li Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan



face recognition, AI face recognition technology, swipe card attendance system, personal information


Traditional swipe card attendance systems for hospitals cannot effectively protect employees’ personal information and ensure that the employees are swiping their own cards. To solve the problem, the present study proposes a novel hospital swipe card attendance system using an artificial intelligence (AI) face modeling system with an open-source face database. The proposed system employs a multi-task cascaded convolutional network (MTCNN) algorithm and FaceNet to improve the performance of face recognition. The system can compare the face of the one who swipes a card with the faces of cardholders in the database, thereby preventing the one from clocking in on behalf of others. The results show that the application of AI technology in the hospital swipe card attendance system can realize the promise of protecting employees’ personal information and verifying employees’ identities.


I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Massachusetts: MIT Press, 2016.

S. Z. Li and A. K. Jain, Handbook of Face Recognition, New York: Springer Science and Business Media, 2005.

H. Mo, L. Liu, W. Zhu, Q. Li, H. Liu, S. Yin, et al., “A Multi-Task Hardwired Accelerator for Face Detection and Alignment,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 11, pp. 4284-4298, November 2020.

K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499-1503, August 2016.

M. Gu, X. Liu, and J. Feng, “Classroom Face Detection Algorithm Based on Improved MTCNN,” Signal, Image, and Video Processing, in press.

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823, June 2015.

J. Hearty, Advanced Machine Learning with Python, Birmingham: Packt Publishing, 2016.

H. Yang and X. Han, “Face Recognition Attendance System Based on Real-Time Video Processing,” IEEE Access, vol. 8, pp. 159143-159150, July 2020.

Q. Miao, F. Xiao, H. Huang, L. Sun, and R. Wang, “Smart Attendance System Based on Frequency Distribution Algorithm with Passive RFID Tags,” Tsinghua Science and Technology, vol. 25, no. 2, pp. 217-226, April 2020.

N. Zhou, R. Liang, and W. Shi, “A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection,” IEEE Access, vol. 9, pp. 5573-5584, January 2021.

B. Yu and D. Tao, “Anchor Cascade for Efficient Face Detection,” IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2490-2501, May 2019.

X. Li, Z. Yang, and H. Wu, “Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 174922-174930, October 2020.

S. Saito, Y. Tomioka, and H. Kitazawa, “A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 9, pp. 2026-2035, September 2017.

N. Merhav, “False-Accept/False-Reject Trade-Offs for Ensembles of Biometric Authentication Systems,” IEEE Transactions on Information Theory, vol. 65, no. 8, pp. 4997-5006, August 2019.

D. H. Kaye, “The Error of Equal Error Rates,” Law, Probability, and Risk, vol. 1, no. 1, pp. 3-8, July 2002.

R. He, J. Cao, L. Song, Z. Sun, and T. Tan, “Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 5, pp. 1025-1037, May 2020.

R. R. Isnanto, A. F. Rochim, D. Eridani, and G. D. Cahyono, “Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images,” International Journal of Engineering and Technology Innovation, vol. 11, no. 1, pp. 45-58, January 2021.

H. Hendrick, A. Aripriharta, S. K. Chen, M. H. Tung, T. C. Chiang, and G. J. Jong, “Nostril in RGB Imaginary by Using NI Vision LabVIEW,” Proceedings of Engineering and Technology Innovation, vol. 4, pp. 37-39, October 2016.




How to Cite

T.-K. Wang, Y.-H. . Lin, and K.-P. Li, “Application of AI Face Recognition Technology in Swipe Card Attendance Systems for Hospitals”, Proc. eng. technol. innov., vol. 21, pp. 01–09, Apr. 2022.