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18 Feb 2021
26 Feb 2020
Informatics, Culture and Technology
20 May 2019
Informatics, Culture and Technology
AUTOMATED STUDENT ATTENDANCE MONITORING SYSTEM IN CLASSROOM BASED ON CONVOLUTIONAL NEURAL NETWORKS
Attending classes by students is associated with the assimilation of educational material by students and the ability to plan and organize activities. However, at present in educational institutions, as a rule, student attendance is recorded manually. Activities are performed frequently and repeatedly, thus wasting instructors' study time. Additionally, the face is one of the most widely used biometric characteristics for personal identification so an automated attendance system using face recognition has been proposed. In recent years, convolutional neural networks (CNN) have become the dominant deep le11arning method for face recognition. In this article, the features of building an automated student attendance system by biometric face recognition using the convolution neural network model has been discussed. Analyzed and solved the main tasks that arise when building an automated student attendance monitoring system: creating a dataset of students' face images; building and training a biometric face recognition model; face recognition from the camera and registration in the database; extension to the face image dataset. The use of the capabilities of the Python and OpenCV libraries is shown. The conducted testing of the accuracy of the developed CNN model of biometric face recognition showed good results – the overall accuracy score is not less than 0.75. The developed automated student attendance monitoring system in classrooms can be used to determine student attendance in different forms of the educational process. Its implementation will significantly reduce the monitoring time and reduce the number of errors in maintaining attendance logs. The introduction of an automated attendance monitoring system will significantly improve the organization of the educational process to ensure its quality
, Dr. of Tech. Sciences, Professor
( firstname.lastname@example.org )
Nhan Cach Dang
, postgraduate student
( email@example.com )
Quoc Tuan Le
, Ph.D, Senior Lecturer
( firstname.lastname@example.org )
The Vinh Tran
, Dr. of Tech. Sciences, Professor
( email@example.com )
Thi Khanh Tien Nguyen
, Ph.D, Senior Lecturer
( firstname.lastname@example.org )
biometric face recognition; convolutional neural network; deep learning; computer vision; face detection; Haar cascade; image processing; face dataset
1. Insaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui & Abdelmalik Taleb-Ahmed. “Past, Present, and Future of Face Recognition: A Review”. Electronics. 2020;Vol. 9 Issue 8. DOI: 10.3390/electronics9081188.
2. Hazim Barnouti N., Sameer Mahmood Al-Dabbagh S. & Esam Matt W. “Face Recognition: A Literature Review”. International Journal of Applied Information Systems (IJAIS). 2016; Vol.11 No.4: 21–31. DOI: 10.5120/ijais2016451597.
3. Aly S. & Hassaballah M. “Face recognition: challenges, achievements and future directions”. IET Computer Vision. 2015; Vol.9 Issue 4. DOI: 10.1049/iet-cvi.2014.0084.
4. Yassin Kortli, Maher Jridi, Ayman Al Falou, and Mohamed Atri “Face Recognition Systems: A Survey”. Sensors, 2020, Vol. 20. Issue 2: 342. DOI: 10.3390/s20020342.
5. Guodong Guo & Na Zhang. “A survey on deep learning based face recognition”. Computer Vision and Image Understanding, 2019, Vol.189. DOI: 10.1016/j.cviu.2019.10280.
6. Youssef Fenjiro. “Face Id: Deep learning for face recognition”. 2019. Retrieved from https://medium.com/@fenjiro/face-id-deep-learning-for-face-recognition-324b50d916d1
7. Parchami Mostafa, Bashbaghi Saman & Granger Eric. “Video-based face recognition using ensemble of haar-like deep convolutional neural networks”. International Joint Conference on Neural Networks (IJCNN). May 2017. Publisher: IEEE, Electronic 2017. DOI:10.1109/IJCNN.2017.7966443.
8. Chen, J., Ranjan, R. & Sankaranarayanan, S. “Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks”. International Journal of Computer Vision. 2018; Vol.126: 272–291. DOI: 10.1007/s11263-017-1029-3.
9. Maheen Zulfiqar, Fatima Syed, Muhammad Jaleed Khan & Khurram Khurshid. “Deep Face Recognition for Biometric Authentication”. Published in: 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). July 2019. DOI: 10.1109/ICECCE47252. 2019.8940725.
10. Goodfellow, I., Yoshua, Y. & Courville, A. “Deep Learning (Adaptive Computation and Machine Learning series)”. MIT Press, 2016. 800 p.
11. Tymchenko, B., Hramatik, A., Tulchiy, H. & Antoshchuk, S. “Classifying mixed patterns of proteinsin microscopic images with deep neural networks”. Herald of Advanced Information Technology. 2019; Vol.2 No.1: 29–36. DOI:10.15276/hait.01.2019.3.
12. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. & Anguelov, D. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2015. DOI: 10.1109/CVPR.2015.7298594.
13. Krizhevsky, A., Sutskever, I. & Hinton G. E. “ImageNet classification with deep convolutional neural networks”. Communications of the ACM. 2017; Vol. 60 No.6: 84–90. DOI: 10.1145/3065386.
14. Nguyen, T., Antoshvhuk, S., Nikolenko, A. & Sotov, V. “Correlation-extreme method for text area localization on images”. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). August 2016. DOI: 10.1109/dsmp.2016.7583534.
15. Schirrmeister, R. T. “Deep learning with convolutional neural networks for EEG decoding and visualization”. Hum. Brain Mapp. 2017; Vol.38 No.11. 5391–5420. DOI: 10.1002/hbm.23730.
16. Khan, M. J., Yousaf, A., Abbas, A. & Khurshid, K “Deep learning for automated forgery detection in hyperspectral document images”. Journal of Electronic Imaging. 2018; Vol.27 Issue 05. DOI: 10.1117/1.JEI.27.5.053001.
17. Henriques, J. F., Caseiro, R., Martins, P. & Batista, J. “High-speed tracking with kernelized correlation filters”. IEEE Transactions on Pattern An2alysis and Machine Intelligence. 2015; 37(3): 583–596. DOI: 0.1109/TPAMI.2014.2345390.
18. Wen Y., Zhang K., Li Z. & Qiao Y. “A Discriminative Feature Learning Approach for Deep Face Recognition”. In: Leibe B., Matas J., Sebe N., Welling M. (eds). Computer Vision – ECCV 2016. Lecture Notes in Computer Science. Springer. 2014; Vol. 9911. DOI: 10.1109/CVPR.2014.242.
19. Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E. & Gool, L. V. “Robust tracking-bydetection using a detector confidence particle filter”. In IEEE International Conference on Computer Vision (ICCV). September 2009. DOI: 10.1109/ICCV.2009.5459278.
20. Xiaojun, L., et al. “Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection”. Mathematical Problems in Engineering. 2017; Vol. 2017. Article ID 1376726. DOI: 10.1155/2017/1376726.
21.Yang, S., et al. “WIDER FACE: A Face Detection Benchmark”. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2016.596
22. Kemelmacher, I., et al. “The MegaFace Benchmark: 1 Million Faces for Recognition at Scale”. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Corpus ID: 7811489. DOI:10.1109/CVPR.2016.527.
23. Huang G. B., et al. “Labeled Faces in the Wild: A Survey”. Advances in Face Detection and Facial Image Analysis. 2016. p. 189–248. DOI: 10.1007/978-3-319-25958-1_8.
24. Chen, J.-C., et al. “Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks”. International Journal of Computer Vision. 2018; 126(2): 272–291. DOI: 10.1007/s11263- 017-1029-3.
25. Szegedy, C., Liu,W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. “Going deeper with convolutions”. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2015.7298594.
26. Wen Y., Zhang K., Li Z., Qiao Y. “A Discriminative Feature Learning Approach for Deep Face Recognition”. In: Leibe B., Matas J., Sebe N., Welling M. (eds). Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science. Springer. 2014; Vol. 9911. DOI: 10.1109/CVPR.2014.242.
27. Liu, W., et al. “SphereFace: Deep Hypersphere Embedding for Face Recognition”. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2017.713.
28. Schroff, F., Kalenichenko D. & Philbin, J. “FaceNet: A unified embedding for face recognition and clustering”. Conference on Computer Vision and Pattern Recognition (CVPR). 2015. p. 815–823. DOI: 10.1109/CVPR.2015.7298682.
29. Wang, H., et al. “CosFace: Large Margin Cosine Loss for Deep Face Recognition”. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Corpus ID: 68589. DOI: 10.1109/CVPR.2018.00552.
30. Deng, J., Guo, J. & Zafeiriou, S. “ArcFace: Additive Angular Margin Loss for Deep Face Recognition”. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2019.00482.
31. Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S. Z. & Hospedales, T. “When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition”. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). DOI: 10.1109/ICCVW.2015.58.
32. Chollet, F. “Deep learning with Python”. USA: Manning Publications. 2018: 384 p.
33. “The TensorFlow tutorials – Convolutional Neural Network”. Available from: https://www.tensorflow.org/tutorials/images/cnn. Tittle from the screen. [Accessed 18th October 2019].
34. “Keras Documentation – Keras API reference”. Available from: https://keras.io/api/. Tittle from the screen. [Accessed 24th October 2019].
35.The Keras Blog by Francois Chollet. “Building powerful image classification models using very little data”. Available from: https://blog.keras.io/building-powerful-image-classification-models-using-very-littledata.html. [Accessed 05th June 2016].
36. Jason Brownlee. “Your First Deep Learning Project in Python with Keras Step-By-Step. Available from: https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/. [Accessed 14th April 2020].
37. Scikit-learn developers. “Machine Learning in Python. Model evaluation: quantifying the quality of predictions.” Available from: https://scikit-learn.org/stable/modules/model_evaluation.html. Tittle from the screen. [Accessed 03th April 2019].
38. Szeliski Richard. “Computer Vision: Algorithms and Applications”. London, UK: Springer. 2010. 812 p. DOI: 10.1007/978-1-84882-935-0.
39. Karan Gupta. “Python OpenCV: Capture Video from Camera”. Available from: https//www.geeksforgeeks.org/python-opencv-capture-video-from-camera. Tittle from the screen. [Accessed 28th January 2020].
40. Rosebrock Adrian. “Practical Python and OpenCV: An Introductory, Example Driven Guide to Image Processing and Computer Vision”. 2016. 166 p.
41. Lienhart, R., Kuranov, E. & Pisarevsky V. “Empirical analysis of detection cascades of boosted classifiers for rapid object detection”. Part of the Lecture Notes in Computer Science book series. 2003; Vol. 2781: 297–304. DOI: 10.1007/978-3-540-45243-0_39.
42. Viola, P. & Jones, M. “Robust real-time face detection”. International Journal of Computer Vision. 2004; Vol. 57: 137–154. DOI: 10.1023/B:VISI.0000013087.49260.fb.
43. Doxygen. “OpenCV – Face Detection using Haar Cascades”. Available from: https://docs.opencv.org/3.4.1/d7/d8b/tutorial_py_face_detection.html. Tittle from the screen. [Accessed 23th February 2020].
44. Wu, S., Kana, M., He, Z., Shan, S. & Chen, X. “Funnel-structured cascade for multi-view face detection with alignment awareness”. Neurocomputing. 2017; Vol. 221: 138–145. DOI: 10.1016/j.neucom.2016.09.072.
45. Resourcifi Inc. “Top 10 Python Libraries for DataScience”. Available from: https://androiddevelopers.co/articles/top-10-python-libraries-for-datascience. [Accessed 01th June 2020].
46.The panda’s development team. “Pandas documentation”. Available from: https://pandas.pydata.org/docs/user_guide/index.html. Tittle from the screen. [Accessed 08th September 2020].
47.Labintcev, E. “Metrics in Machine Learning Problems”. Available from: https://habr.com/ru/company/ods/blog/328372/. Tittle from the screen. [Accessed 12th May 2017].
Received after revision 10.09.2020
Vol. 3 № 3, 2020
11 May 2021
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