Skip to main content
SpringerLink
Log in

Real Time Automatic Attendance System for Face Recognition Using Face API and OpenCV

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Traditionally, the attendance of students has been a major concern for the colleges and the faculty has to spend a lot of time and is a tedious job to mark attendance manually. Current biometric attendance system is not automatic that’s why wastes a lot of time, difficult to maintain and requires a queue for scanning fingerprints to mark their attendance. In Modern era everyone has Smartphone and connected via internet every time. In this paper attendance monitoring will be done through smart phone available with almost all faculty members. Some of popular object detection algorithms are back propagation neural network, region based convolution network (RCNN), faster RCNN, single shot detector. Our unified structure is based on YOLO V3 (You only look once) algorithm for face detection and Microsoft Azure using face API for face recognition (face database). The unique part is camera installed in classroom will take picture twice one at the start and one at the end to ensure students has attended complete class. YOLO V3 will first count the students in an image followed by identifying faces as known and unknown generating spreadsheets separately and an email is send at the end of month to students, parents and faculty. The designed system performs efficient in real time implementation for counting and detection. Our entire system has proven to gather high accuracy in face detection and performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

REFERENCES

  1. Fawaz, A. (2019). A smart classroom of wireless sensor networks for students time attendance system. In IEEE integrated STEM education conference (ISEC).

  2. Helmi, R., Yusuf, S., & Jamal, A. (2019). Face recognition automatic class attendance system (FRACAS). In IEEE international conference on automatic control and intelligent systems (I2CACIS 2019), Selangor, Malaysia, June 29, 2019.

  3. Xing, Y. (2019). A class attendance system based on SL4A. In IEEE 4th international conference on cloud computing and big data analytics.

  4. Mijić, D., Durutović, J., Bjelica, O., & Ljubojević, M. (2019). An improved version of student attendance management system based on RFID. In 18th international symposium INFOTEH-JAHORINA, March 20–22, 2019.

  5. McCluskey, C. P., Bynum, T. S., & Patchin, J. W. (2004). Reducing chronic absenteeism: An assessment of an early truancy initiative. NCCD News,50(2), 214–234.

    Google Scholar 

  6. Li, J., Gu, J., Huang, Z., & Wen, J. (2019). Application research of improved YOLO V3 algorithm in PCB electronic component detection. Applied Sciences,9, 3750. https://doi.org/10.3390/app9183750.

    Article  Google Scholar 

  7. Arsenovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2017). FaceTime—Deep learning based face recognition attendance system. In IEEE 15th international symposium on intelligent systems and informatics. University of Novi Sad, SISY 2017, September 14–16, Subotica, Serbia.

  8. Varadharajan, E., Dharani, R., Jeevitha, S., Kavinmathi, B., & Hemalatha, S. (2016). Automatic attendance management system using face detection. In Online international conference on green engineering and technologies (IC-GET).

  9. Shoewu, O., & Idowu, O. A. (2012). Development of attendance management system using biometrics. The Pacific Journal of Science and Technology,13(1), 300–307.

    Google Scholar 

  10. Yadav, V., & Bhole, G. P. (2019). Cloud based smart attendance system for educational institutions. In 2019 international conference on machine learning, big data, cloud and parallel computing (Com-IT-Con), India, Feb 14th–16th, 2019.

  11. Albuhairi, T., & Altameem, A. (2019). Using RFID technology in vaccination cards for Saudi children. In First international conference on sustainable technologies for computational intelligence, pp. 125–137.

  12. Das, S. (2017). CNN architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more ….. Medium, November 16, 2017 [Online]. Retrieved September 22, 2018 from https://medium.com/@sidereal/cnns-architectures-lenet- alexnet-vgg-googlenet-resnet-and-more-666091488df5.

  13. Zhao, Z.-Q., Zheng, P., Xu, S., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems,30(11), 3212–3232.

    Article  Google Scholar 

  14. Rosebrock, A. (2018). YOLO object detection with OpenCV. Py Image Search. Retrieved December 20, 2018.

  15. Maj, M. (2018). What is object detection? Introduction to YOLO algorithm. Appsilon Data Science. Retrieved December 18, 2018 from https://appsilon.com/object-detection-yolo-algorithm.

  16. Geethapriya, S., Duraimurugan, N., & Chokkalingam, S. P. (2019). Real-time object detection with Yolo. International Journal of Engineering and Advanced Technology (IJEAT),8(3S), 1–4. https://www.ijeat.org/wp-content/uploads/papers/v8i3S/C11240283S19.pdf.

  17. Kim, C., Lee, J., Han, T., & Kim, Y.-M. (2018). A hybrid framework combining background subtraction and deep neural networks for rapid person detection. Journal of Big Data,1, 1. https://doi.org/10.1186/s40537-018-0131-x.

    Article  Google Scholar 

Download references

Acknowledgement

The authors are thankful to Ms. Nighat Usman for the valuable reviews, suggestions, and comments.

Author information

Authors and Affiliations

  1. Department of Telecom Engineering, UET, Taxila, Pakistan

    Sikandar Khan & Adeel Akram

  2. Department of Computer Sciences, BULC, Lahore, Pakistan

    Nighat Usman

Authors
  1. Sikandar Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adeel Akram
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nighat Usman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sikandar Khan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, S., Akram, A. & Usman, N. Real Time Automatic Attendance System for Face Recognition Using Face API and OpenCV. Wireless Pers Commun 113, 469–480 (2020). https://doi.org/10.1007/s11277-020-07224-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07224-2

Keywords

Search

Navigation