Main Article Content
Abstract - This research focuses on developing an AI-enabled Contactless Visitors Access Monitoring System. The monitoring system integrated a facial recognition system with a real-time database. Visitors registered themselves through an online registration form. This research developed and compared two different facial recognition systems. The first facial recognition system integrated the dlib model with the face recognition library, while the second integrated the FaceNet model with the Haar Cascade Classifier. Twenty facial images were collected. The researcher found out that the facial recognition system with FaceNet has higher accuracy of 82% while the has 76% of accuracy. The value of EER obtained for FaceNet is at 51% with an allowed threshold of 0.52. This research found that the accuracy of the facial recognition system could be affected by different conditions, such as the visitors’ facial features, the distance between the camera and the face, and the illumination condition of the test environment. The number of images does not affect the speed and the accuracy of the facial recognition system in this research due to the small number of images.
(Manuscript received: 26 June 2023 | Accepted: 1st September 2023 | Published: 30 September 2023)
Adjabi, I. et al. (2020) “Past, present, and future of Face Recognition: A Review,” Electronics, 9(8), p. 1188. Available at: https://doi.org/10.3390/electronics9081188.
Carikci, M. and Ozen, F. (2012) “A face recognition system based on eigenfaces method,” Procedia Technology, 1, pp. 118–123. Available at: https://doi.org/10.1016/j.protcy.2012.02.023.
Singh, B.R. et al. (2019) “Microbiology research international,” Bacteria on fingerprint scanners of biometric attendance machines, 7(4), pp. 31–39. Available at: https://doi.org/10.30918/mri.
Jagtap, A.M. et al. (2019) ‘A study of LBPH, Eigenface, Fisherface and haar-like features for face recognition using opencv’, 2019 International Conference on Intelligent Sustainable Systems (ICISS) [Preprint]. doi:10.1109/iss1.2019.8907965.
Ghorbani, M., Targhi, A.T. and Dehshibi, M.M. (2015) “Hog and LBP: Towards a robust face recognition system,” 2015 Tenth International Conference on Digital Information Management (ICDIM) [Preprint]. Available at: https://doi.org/10.1109/icdim.2015.7381860.
Ren, H. (2019) “A comprehensive study on robustness of hog and LBP towards image distortions,” Journal of Physics: Conference Series, 1325(1), p. 012012. Available at: https://doi.org/10.1088/1742-6596/1325/1/012012.
King, D.E. (2009) Dlib-ml: A machine learning toolkit - jmlr.org, Journal of Machine Learning Research. Available at: https://www.jmlr.org/papers/volume10/king09a/king09a.pdf (Accessed: April 14, 2023).
Chen, L. et al. (2021) ‘Driver fatigue detection based on facial key points and LSTM’, Security and Communication Networks, 2021, pp. 1–9. doi:10.1155/2021/5383573.
Mohanty, S. et al. (2019) “Design of real-time drowsiness detection system using Dlib,” 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) [Preprint]. Available at: https://doi.org/10.1109/wiecon-ece48653.2019.9019910.
Viola, P. and Jones, M. (2001) “Rapid object detection using a boosted cascade of Simple features,” Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 [Preprint]. Available at: https://doi.org/10.1109/cvpr.2001.990517.
Rawat, W. and Wang, Z. (2017) ‘Deep convolutional neural networks for Image Classification: A Comprehensive Review’, Neural Computation, 29(9), pp. 2352–2449. doi:10.1162/neco_a_00990.
Vaz, D. et al. (2023) ‘Mires: Intrusion recovery for applications based on backend-as-a-service’, IEEE Transactions on Cloud Computing, 11(2), pp. 2011–2027. doi:10.1109/tcc.2022.3178982.
Guo, D. and Onstein, E. (2020) ‘State-of-the-art geospatial information processing in NoSQL databases’, ISPRS International Journal of Geo-Information, 9(5), p. 331. doi:10.3390/ijgi9050331.
Abdullah, E. et al. (2021) Development of Real-Time Energy Monitoring System and Data Log Using NodeMCU ESP 8266 and MYSQL Database, 10 (1), pp. 245–262.
Hiremani, N. et al. (2022) “Artificial Intelligence-powered contactless face recognition technique for internet of Things Access for Smart Mobility,” Wireless Communications and Mobile Computing, 2022, pp. 1–11. Available at: https://doi.org/10.1155/2022/8750840.
Bong, Y.-S. and Lee, G.-C. (2021) “A contactless visitor access monitoring system,” International Journal on Robotics, Automation and Sciences, 3, pp. 33–41. Available at: https://doi.org/10.33093/ijoras.2021.3.6.
Teoh, K.H. et al. (2021) “Face recognition and identification using deep learning approach,” Journal of Physics: Conference Series, 1755(1), p. 012006. Available at: https://doi.org/10.1088/1742-6596/1755/1/012006.
Jain, V. and Patel, D. (2016) “A GPU based implementation of robust face detection system,” Procedia Computer Science, 87, pp. 156–163. Available at: https://doi.org/10.1016/j.procs.2016.05.142.