Automatic Attendance System Using Deep Learning Framework

  • Pinaki Ranjan SarkarEmail author
  • Deepak Mishra
  • Gorthi R. K. Sai Subhramanyam
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


Taking attendance in a large class is cumbersome, repetitive, and it consumes valuable class time. To avoid these problems, we propose an automatic attendance system using deep learning framework. An automatic attendance system based on the image processing consists of two steps: face detection and face recognition. Face detection and recognition are well-explored problems in computer vision domain, though they are still not solved due to large pose variations, different illumination conditions, and occlusions. In this work, we used state-of-the-art face detection model to detect the faces and a novel recognition architecture to recognize faces. The proposed face verification network is shallower than the state-of-the-art networks and it has achieved similar face recognition performance. we achieved 98.67% on LFW and 100% on classroom data. The classroom data was made by us for practical implementation of the complete network during this work.


  1. 1.
    Bansal, A., Castillo, C.D., Ranjan, R., Chellappa, R.: The do’s and don’ts for cnn-based face verification. CoRR abs/1705.07426 (2017).
  2. 2.
    Chintalapati, S., Raghunadh, M.: Automated attendance management system based on face recognition algorithms. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–5. IEEE (2013)Google Scholar
  3. 3.
    Hassaballah, M., Aly, S.: Face recognition: challenges, achievements and future directions. IET Comput. Vis. 9(4), 614–626 (2015)CrossRefGoogle Scholar
  4. 4.
    Hassner, T., Harel, S., Paz, E., Enbar, R.: Effective face frontalization in unconstrained images. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 7, 4295–4304 (2015)Google Scholar
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2016, 770–778 (2016)Google Scholar
  6. 6.
    Hu, P., Ramanan, D.: Finding tiny faces. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1522–1530 IEEE (2017)Google Scholar
  7. 7.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report, Technical Report 07–49, University of Massachusetts, Amherst (2007)Google Scholar
  8. 8.
    Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Adv. Neural Inf. Process. Syst. 2017–2025 (2015)Google Scholar
  9. 9.
    Lu, C., Tang, X.: Surpassing human-level face verification performance on lfw with gaussianface. AAAI, 3811–3819 (2015)Google Scholar
  10. 10.
    Lukas, S., Mitra, A.R., Desanti, R.I., Krisnadi, D.: Student attendance system in classroom using face recognition technique. In: International Conference on Information and Communication Technology Convergence (ICTC), pp. 1032–1035. IEEE (2016)Google Scholar
  11. 11.
    Rekha, E., Ramaprasad, P.: An efficient automated attendance management system based on eigen face recognition. In: 7th International Conference on Cloud Computing, Data Science and Engineering-Confluence, pp. 605–608. IEEE (2017)Google Scholar
  12. 12.
    Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar
  13. 13.
    Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp. 1988–1996 (2014)Google Scholar
  14. 14.
    Sun, Y., Liang, D., Wang, X., Tang, X.: Deepid3: face recognition with very deep neural networks (2015). arXiv:1502.00873
  15. 15.
    Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2892–2900 (2015)Google Scholar
  16. 16.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 1701–1708 (2014)Google Scholar
  17. 17.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001, pp. I–I. IEEE (2001)Google Scholar
  18. 18.
    Wagh, P., Thakare, R., Chaudhari, J., Patil, S.: Attendance system based on face recognition using eigen face and pca algorithms. In: International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 303–308. IEEE (2015)Google Scholar
  19. 19.
    Zhong, Y., Chen, J., Huang, B.: Towards end-to-end face recognition through alignment learning (2017). arXiv:1701.07174
  20. 20.
    Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: A 3d solution. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 146–155 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pinaki Ranjan Sarkar
    • 1
    Email author
  • Deepak Mishra
    • 1
  • Gorthi R. K. Sai Subhramanyam
    • 2
  1. 1.Indian Institute of Space Science and TechnologyThiruvananthapuramIndia
  2. 2.Indian Institute of Technology TirupatiTirupatiIndia

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