Deep Learning in Face Recognition for Attendance System: An Exploratory Study

Authors

  • Mochamad Azkal Azkiya Aziz Albukhary International University
  • Shahrinaz Ismail Albukhary International University
  • Noormadinah Allias Albukhary International University

DOI:

https://doi.org/10.24191/jcrinn.v7i2.288

Keywords:

face recognition, Local Binary Network, Local Binary Pattern Histogram, deep learning, attendance system

Abstract

Conventional-manual type of attendance systems can be very time-consuming to some extent, particularly for a significant number. The existence of face recognition technology can solve the inefficiency and ineffectiveness of conventional and manual attendance systems. Among many approaches to implement face recognition, this research focuses on using deep learning approaches as it has been proven to give promising results. There are various algorithms for face recognition, such as Local Binary Pattern Histogram (LBPH), Local Binary Pattern Network (LBPn), Haar Cascade, and Convolutional Neural Network. The use of deep learning can reach 98 percent accuracy. However, it is necessary to conduct further research on its implementation on the real system in order to evaluate the efficiency of the system.  An interview was conducted with an expert in the field, to understand the concept, trend, and use of deep learning in face recognition, as well as to determine the suitable algorithm for the attendance system.  This paper presents the results from this interview, which provide an insight based on real practices.

Downloads

Download data is not yet available.

References

AlBdairi, A. J., Xiao, Z., Alkhayyat, A., Humaidi, A. J., Fadhel, M. A., Taher, B. H., Alzubaidi, L., Santamaría, J., & Al-Shamma, O. (2022). Face recognition based on Deep Learning and FPGA for ethnicity identification. Applied Sciences, 12(5). https://doi.org/10.3390/app12052605

Deng, L. & Yu, D. (2014). Deep Learning: Methods and Applications, Foundations and Trends in Signal Processing, 7(3), 197-387. https://doi.org/10.1561/2000000039

Dusa, P., & Phulpagar, B. D. (2020). Criminal Detection Using OpenCV DNN and OpenCV LBPH Methods. Aegaeum Journal, 8(8), 675-684. https://doi.org/16.10089.AJ.2020.V8I8.285311.3967

Farfade, S. S., Saberian, M. J. & Li, L.J. (2015). Multiview Face Detection using Deep Convolutional Neural Networks, 5th ACM on International Conference on Multimedia Retrieval, Shanghai, 2015, 643-650. https://doi.org/10.1145/2671188.2749408

Grand View Research. (2020). Facial Recognition Market Size, Share & Trends Analysis Report by Technology (2D, 3D, Facial Analytics), by Application (Access Control, Security & Surveillance), by End-use, by Region and Segment Forecasts, 2021-2028. Retrieved August 01, 2022, from https://www.grandviewresearch.com/industry-analysis/facial-recognition-market

Guo, G., & Zhang, N. (2019). A survey on deep learning-based face recognition. Computer vision and image understanding, 189. https://doi.org/10.1016/j.cviu.2019.102805.

Ismail, S., & Ismail, S. (2022). A preliminary study of cashless payment face recognition system development in Malaysia. 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), Seoul, 2022, 1-5. https://doi.org/10.1109/IMCOM53663.2022.9721723

Kar, N., Debbarma, M. K., Saha, A., & Pal, D. R. (2012). Study of implementing automated attendance system using face recognition technique. International Journal of Computer and Communication Engineering, 1(2), 100–103. https://doi.org/10.7763/ijcce.2012.v1.28

Patel, S., Kumar, P., Garg, S., & Kumar, R. (2018). Face recognition based smart attendance system using IOT. International Journal of Computer Sciences and Engineering, 6(5), 871–877. https://doi.org/10.26438/ijcse/v6i5.871877

Pei, Z., Xu, H., Zhang, Y., Guo, M., & Yang, Y.-H. (2019). Face recognition via deep learning using data augmentation based on orthogonal experiments. Electronics, 8(10). https://doi.org/10.3390/electronics8101088

Setiowati, S., Zulfanahri, Franita, E. L., & Ardiyanto, I. (2017). A review of optimization method in face recognition: Comparison deep learning and non-deep learning methods. 9th International Conference on Information Technology and Electrical Engineering (ICITEE), Puket, 2017, 1-6. https://doi.org/10.1109/ICITEED.2017.8250484

Singhal, N., Ganganwar, V., Yadav, M., Chauhan, A., Jakhar, M., & Sharma, K. (2021). Comparative study of machine learning and Deep Learning Algorithm for face recognition. Jordanian Journal of Computers and Information Technology, 7(3), 313-325. https://doi.org/10.5455/jjcit.71-1624859356

ST, S., Ayoobkhan, M. U., V, K. K., Bacanin, N., K, V., Štěpán, H., & Pavel, T. (2022). Deep learning model for deep fake face recognition and detection. PeerJ Computer Science, 8. https://doi.org/10.7717/peerj-cs.881

Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014, 1701-1708. https://doi.org/10.1109/CVPR.2014.220

Teoh, K. H., Ismail, R. C., Naziri, S. Z. M., Hussin, R., Isa, M. N. M. & Basir, M. S. S. M. (2021). Face Recognition and Identification using Deep Learning Approach, Journal of Physics: Conference Series, 1755. https://doi.org/10.1088/1742-6596/1755/1/012006

Tolba, A. S., El-Baz, A. H., & El-Harby, A. A. (2006). Face recognition: A literature review. International Journal of Signal Processing, 2(2), 88-103.

Trigueros, D. S., Li, M. & Hartnett, M. (2018). Face Recognition: From Traditional to Deep Learning Methods, Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.1811.0011

Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71–86. https://doi.org/10.1162/jocn.1991.3.1.71

Xi, M., Chen, L., Polajnar, D., & Tong, W. (2016). Local Binary Pattern Network: A deep learning approach for face recognition. IEEE International Conference on Image Processing (ICIP), Phoenix, 2016, 3224-3228. https://doi.org/10.1109/ICIP.2016.7532955

Zangeneh, E., Rahmati, M., & Mohsenzadeh, Y. (2020). Low resolution face recognition using a two-branch deep convolutional neural network architecture. Expert Systems with Applications, 139. https://doi.org/10.1016/j.eswa.2019.112854

Downloads

Published

2022-09-30

How to Cite

Azkiya Aziz, M. A., Ismail, S., & Allias, N. (2022). Deep Learning in Face Recognition for Attendance System: An Exploratory Study. Journal of Computing Research and Innovation, 7(2), 74–81. https://doi.org/10.24191/jcrinn.v7i2.288

Issue

Section

General Computing