Real-Time Face Recognition System with Enhanced Security Features using Deep Learning

Mukta Banerjee; Riya Goyal; Pragati Gupta; Ashish Tripathi

doi:10.52756/ijerr.2023.v32.011

Authors

Mukta Banerjee Department of Information Technology, G. L. Bajaj Institute of Technology and Management, Greater Noida, India https://orcid.org/0009-0004-2516-2820
Riya Goyal Department of Information Technology, G. L. Bajaj Institute of Technology and Management, Greater Noida, India https://orcid.org/0009-0004-8516-4633
Pragati Gupta School of Computer Science and Engineering, Galgotias University, Greater Noida, India https://orcid.org/0009-0006-1654-5736
Ashish Tripathi School of Computer Science and Engineering, Galgotias University, Greater Noida, India https://orcid.org/0000-0002-7570-4067

DOI:

https://doi.org/10.52756/ijerr.2023.v32.011

Keywords:

Face Recognition, Deep Learning, YOLOv5, Viola Jones, Security

Abstract

Abstract: Identification of people and mask detection has long been a captivating topic, in terms of research and business. This topic has received increasing attention in recent phases due to the speedy advancement of Artificial Intelligence (AI). Nowadays, a lot of applications, including phone unlocking systems, criminal identification systems, and even home security systems, use face recognition as a common technique. Due to the fact that this method only requires a facial image instead of other dependencies like a key or card, it is more secure. Face detection and face identification are often the first two elements of a human recognition system. Even during COVID-19, it is considered the best way to stop the spread of the COVID-19 virus is by wearing a face mask. The risk of contracting the virus can be reduced by almost 70% only by wearing a face mask. In order to promote community health. This Study aims to produce a highly precise and real-time method that can effectively recognize people and identify non-mask faces in public. When a person stands in front of the device, this application detects the human face automatically using detection, extraction, and recognition algorithms. The proposed work applies the Viola-Jones algorithm for face recognition and the YOLOv5 algorithm for mask detection and classification. When the proposed work is tested, this shows higher accuracy in mask detection which is 92.8%.

References

Amodeo, C. (2022). FCS-GVAE: A Deep Graph Unsupervised Learning Approach For The Analysis Of The Human Connectome (Doctoral dissertation, University of Illinois at Chicago).

Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, 100071. https://doi.org/10.1016/j.dajour.2022.100071

Batta, L. A., & Tuncer, A. (2022). Detection of Face Mask Wearing Condition for COVID-19 Using Mask R-CNN. El-Cezeri, 9(3), 1051-1060.

Campbell, C., & Ying, Y. (2022). Learning with support vector machines. Springer Nature.

Chaudhari, M.N., Deshmukh, M., Ramrakhiani, G. and Parvatikar, R., 2018, August. Face detection using viola jones algorithm and neural networks. IEEE, In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) pp. 1-6. https://doi.org/10.1109/ICCUBEA.2018.8697768

Deepak (2021). face_mask_detection_yolov5. Accessed from Kaggle. Source: https://www.kaggle.com/datasets/deepakat002/face-mask-detection-yolov5

Gupta, S., Sreenivasu, S.V.N., Chouhan, K., Shrivastava, A., Sahu, B., & Potdar, R.M. (2023). Novel face mask detection technique using machine learning to control COVID’19 pandemic. Materials Today: Proceedings, 80, 3714-3718. https://doi.org/10.1016/j.matpr.2021.07.368

Horvat, M., & Gledec, G. (2022). A comparative study of YOLOv5 models performance for image localization and classification. In Central European Conference on Information and Intelligent Systems. Faculty of Organization and Informatics Varazdin, pp. 349-356.

Howell, A.J. (2022). Introduction to face recognition. In Intelligent biometric techniques in fingerprint and face recognition. Routledge, pp. 217-283. https://doi.org/10.1201/9780203750520-7

Hussain, D., Ismail, M., Hussain, I., Alroobaea, R., Hussain, S., & Ullah, S.S. (2022). Face mask detection using deep convolutional neural network and MobileNetV2-based transfer learning. Wireless Communications and Mobile Computing, 2022, 1-10. https://doi.org/10.1155/2022/1536318

Indulkar, Y., 2021, June. Alleviation of COVID by means of Social Distancing & Face Mask Detection Using YOLO V4. IEEE. In 2021, International conference on communication information and computing technology (ICCICT), pp. 1-8. https://doi.org/10.1109/ICCICT50803.2021.9510168

Jiang, X., Gao, T., Zhu, Z., & Zhao, Y. (2021). Real-time face mask detection method based on YOLOv3. Electronics, 10(7), 837. https://doi.org/10.3390/electronics10070837

Kaur, J., Sharma, A., & Cse, A. (2017). Performance analysis of face detection by using Viola-Jones algorithm. International Journal of Computational Intelligence Research, 13(5), 707-717.

Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 42. https://doi.org/10.3390/s20020342

Leo, M.J., & Suchitra, S. (2018). SVM Based Expression-Invariant 3D Face Recognition System. Procedia Computer Science, 143, 619-625. https://doi.org/10.1016/j.procs.2018.10.441

Li, Y. (2022). January. Research and application of deep learning in image recognition. In 2022 IEEE, 2nd International Conference on Power, Electronics and Computer Applications (ICPECA). pp. 994-999. https://doi.org/10.1109/ICPECA53709.2022.9718847

Nagoriya, H., & Parekh, M. (2021). Live Facemask Detection System. Int. J. Imaging. Robot., 21(1), 1-8.

Natarajan, V.A., Vardhan, P.V., Priya, N.M.S., Vineeth, N., & Parthu, V. (2022). December. Real-Time Face Mask Detection from CCTV Video Frames using Deep Neural Networks. IEEE, In 2022, International Conference on Automation, Computing and Renewable Systems (ICACRS), pp. 809-812. https://doi.org/10.1109/ICACRS55517.2022.10029096

Pebrianto, W., Mudjirahardjo, P., & Pramono, S.H. (2022). YOLO method analysis and comparison for real-time human face detection. In IEEE, 11th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), pp. 333-338. https://doi.org/10.1109/EECCIS54468.2022.9902919

Peng P, Portugal I, Alencar P, Cowan D. A face recognition software framework based on principal component analysis. PLoS One, 16(7), e0254965. https://doi.org/10.1371/journal.pone.0254965

Pranav, K.B., & Manikandan, J. (2020). Design and evaluation of a real-time face recognition system using convolutional neural networks. Procedia Computer Science, 171, 1651-1659. https://doi.org/10.1016/j.procs.2020.04.177

Rana, S. and Singh, A., 2016, October. Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques. IEEE, In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 106-111. https://doi.org/10.1109/NGCT.2016.7877399

Redmon, J., Divvala, S., Girshick, R., Farhadi A., 2015 You Only Look Once: Unified, Real-Time Object Detection, Source: https://arxiv.org/abs/1506.02640. https://doi.org/10.1109/CVPR.2016.91

Sapakova, S., & Yilibule, Y. (2022). Deep Learning-Based Face Mask Detection Using Yolov5 Model. Scientific Journal of Astana IT University, pp.5-13. https://doi.org/10.37943/12TXQS9259

Shatnawi, Y., Alsmirat, M., Al-Ayyoub, M., & Aldwairi, M. (2018). The impact of the number of eigen-faces on the face recognition accuracy using different distance measures. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1-5. https://doi.org/10.1109/AICCSA.2018.8612837

Solomon, J.M.M., Meena, M.S., & Kaur, J. (2019). Challenges in face recognition systems. IJRAR, 6(2).

Srivastava, S., Divekar, A.V., Anilkumar, C., Naik, I., Kulkarni, V. and Pattabiraman, V., 2021. Comparative analysis of deep learning image detection algorithms. Journal of Big Data, 8(1), 1-27. https://doi.org/10.1186/s40537-021-00434-w

Suyal, M., & Goyal, P. (2022). A Review on Analysis of K-Nearest Neighbor Classification Machine Learning Algorithms based on Supervised Learning. International Journal of Engineering Trends and Technology, 70(7), 43-48. https://doi.org/10.14445/22315381/IJETT-V70I7P205

Schenkel, T., Ringhage, O., & Branding, N. (2019). A Comparative Study of Facial Recognition Techniques: With focus on low computational power.

Upendra, H.S., Suman, S., Vishnu, S.S., & Dharani, J. (2021). Real-Time Face Mask Detection using OpenCV and Deep Learning. In ICET