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Cited by 19Face recognition and analysisFace and Expression RecognitionBiometric Identification and Security

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AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study

Pattnaik, Priyanka, Mohanty, Kalyan Kumar·2020·2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)
DOI 10.1109/iceca49313.2020.9297643
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Abstract

Face recognition is a powerful tool for a biometric system that takes data from both images and videos. The traditional attendance system can be replaced by the automatic attendance system to utilize class time more effectively. In this paper real-time, attendance monitoring uses a web app that can be operated remotely by using a local server and Amazon Web Service (AWS) cloud recognition Application Programming Interface (API). The first approach follows five sections which are face detection, preprocessing, training and, face recognition through which attendance will be recorded and mailed to the respective teacher. The second approach is based on AWS recognition API which processes the data in the cloud.

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Research works printed but not linked: 21

  1. 10.1109/ICDAMT.2018.8376533 · DOI
  2. 10.1007/s00371-020-01831-7 · DOI
  3. yang, 2018, Real-time face detection vased on YOLO, In 1st IEEE International Conference on Knowledge Innovation and Invention 2018, 221, 10.1109/ICKII.2018.8569109 · DOI · Paper page
  4. 10.1016/0031-3203(95)00067-4 · DOI · Paper page
  5. huang, 2016, Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors, Remote Sens mdpi, 8, 483, 10.3390/rs8060483 · DOI
  6. 10.1109/ACCESS.2018.2825310 · DOI
  7. barik, 2017, Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data, International Journal of Information Technology and Computer Science, 4, 73
  8. parmar, 2014, Face recognition methods & applications, arXiv preprint arXiv 1403 0485
  9. 10.1109/TPAMI.2006.244 · DOI
  10. 10.1109/ICCSN.2011.6014865 · DOI
  11. tan, 2010, Enhanced local texture feature sets for face recognition under difficult lighting conditions, IEEE Transactions on Image Processing, 19, 1635, 10.1109/TIP.2010.2042645 · DOI
  12. 10.1109/TSMCB.2006.870645 · DOI
  13. 10.1016/j.eswa.2019.112854 · DOI
  14. 10.1109/PARC49193.2020.236628 · DOI
  15. 10.1109/ACCESS.2019.2918275 · DOI
  16. 10.1016/j.jvcir.2019.02.014 · DOI
  17. 10.1109/CVPR.1991.139758 · DOI
  18. meethongjan, 2007, A Summary of literature review: Face Recognition, Proceedings of the Postgraduate Annual Research Seminar
  19. kadir, 2014, A comparative study between lbp and haar-like features for face detection using opencv, 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T) ICE2T, 335, 10.1109/ICE2T.2014.7006273 · DOI
  20. 10.1016/j.future.2017.11.013 · DOI
  21. 10.36548/jitdw.2020.1.006 · DOI

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Metadata

Authors
Pattnaik, P., & Mohanty, K. K.
Venue
2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)
Publisher
IEEE
Published
2020-11-05
Pages
1034-1039
DOI
10.1109/iceca49313.2020.9297643
OpenAlex
W3115179344
Cited by
19
References
0
Citations by year
2026: 1 · 2025: 3 · 2024: 7 · 2023: 3 · 2022: 4 · 2021: 1
FWCI
0.62
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Electronics, Communication and Aerospace Technology (ICECA) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
Keywords
face recognition, attendance system, real-time attendance monitoring, biometric authentication, face detection, web application, email notifications

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CoChat. (2026). Citation check: AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study. https://app.cochat.ai/papers/10.1109/iceca49313.2020.9297643
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Pattnaik, P., & Mohanty, K. K. (2020). AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA). https://doi.org/10.1109/iceca49313.2020.9297643