Interactive Attendance System for Modern Education Using Computational Intelligence

Kemal Polat

1. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology

2. Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey, Email: [email protected]

*Corresponding Author

The modern age is marked exclusively by advancements in digital technology. Modern education is not untouched by this advancement. However, there are few systems at present, which use computational intelligence in modern education. Currently, digital technology is bounded by commercial uses. In particular, face recognition is mainly used in criminal identification, security, payment, advertising, and healthcare. This project, Interactive Attendance System For Modern Education Using Computational Intelligence, is an attempt to combine modern education with modern computing. It uses the concept of Face Recognition to identify the person and to create an attendance file. The coding is done in the Python language on a Raspberry Pi 3B+ kit with Raspbian. Image pre-processing is used to convert captured images into grayscale and to extract areas of interest, i.e., faces. Machine learning is used to train the system to recognize faces using the LBPH Face Recognizer. This system can be installed in any modern educational institute for smart attendance.

Face Recognition, Raspberry Pi 3B+, LBPH Face Recognizer, OpenCV, Attendance File

V. Dhilip Kumar, Md Meraj Alam, and Kemal Polat (2021). Interactive Attendance System for Modern Education Using Computational Intelligence. Journal of the Institute of Electronics and Computer, 3, 75-86. https://doi.org/10.33969/JIEC.2021.31006.

[1] Deep Attribute Guided Representation for Heterogeneous Face Recognition, Liu, Decheng and Wang, Nan-nan and Peng, Chunlei and Li, Jie and Gao, Xinbo, http://www.ijcai.org/proceedings/2018/0116.pdf, 2018

[2] Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks, Goswami, Gaurav and Ratha, Nalini and Agarwal, Akshay and Singh, Richa and Vatsa, Mayank, arXiv:1803.00401v1, 2018

[3] Deep Sketch-Photo Face Recognition Assisted by Facial Attributes, Iranmanesh, Seyed Mehdi and Kazemi, Hadi and Soleymani, Sobhan and Dabouei, Ali and Nasrabadi, Nasser M., http://arxiv.org/abs/1808.00059, 2018

[4] Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review, Plichoski, Guilherme Felippe and Chi-dambaram, Chidambaram and Parpinelli, Rafael Stubs, Revista Brasileira de Computac~ao Aplicada, http://seer.upf.br/index.php/rbca/article/view/8046, 2018 (vol-10)

[5] A Survey on Face Recognition Based Security System and its Applications, Kalita, Nijara and Saikia, International Research Journal of Engineering and Technology, 2018 (vol-5)

[6] Multicolumn Networks for Face Recognition, Xie, Weidi and Zisserman, Andrew, http://arxiv.org/abs/1807.09192, 2018

[7] Wild patterns: Ten years after the rise of adversarial machine learning, Biggio, Battista and Roli, Fabio,  Pattern Recognition, 2018 (vol-84)

[8] Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insuficient Labeled Samples, Gao, Yuan and Ma, Jiayi and Yuille, Alan L., IEEE Transactions on Image Processing, 2017 (vol-26)

[9] A Survey on Deep Learning in Medical Image Analysis, Litjens, Geert and Kooi, Thijs and Be-jnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and van der Laak, Jeroen A. W. M. And van Ginneken, Bram and Sanchez, Clara I., http://arxiv.org/abs/1702.05747http://dx.doi.org/10.1016/j.media.2017.07.005, 2017

[10] Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning, Lema^tre, Guillaume and Nogueira, Fernando and  Aridas, Christos  K., The Journal of Machine Learning Re-search, http://www.jmlr.org/papers/volume18/16-365/16-365.pdfhttps://dl.acm.org/citation.cfm, 2017 (vol-18)