Authors : Lawrence Rodriques; Swati Padmanabhan; Sumit Prasad; Neha Auti; Nupur Gaikwad

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/2yatxdnf

Scribd : https://tinyurl.com/4jre99z9

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1519

Abstract : Security in restricted areas is essential for protecting valuable assets, sensitive information, ensuring the safety from intruders. Traditional security systems have many limitations, where they cannot authenticate whether the entered person is an intruder or not. To address this challenge by implementing a real-time face recognition- based surveillance system, is the goal of this project. Realtime Intrusion detection system provides surveillance for restricted and confidential areas with help of face recognition and detection, when an intruder or unauthorized person enters the area, this system will give an alert to the respective in charge through various channels, including email, messaging services, and direct phone calls. In this system, the OpenCV python library along with several algorithms are used to abstract the facial features and to take the input dataset. For face detection, the system utilizes SCRFD and YOLO, and it employs Arcface for accurate face recognition. This technique ensures the system can distinguish between an intruder and an authorized individual entering the secured area. This proactive approach enhances surveillance efficiency and reinforces the safety and integrity of restricted areas. For instance, when an individual enters the restricted area, the system captures and analyses their face. It then verifies whether the detected face matches any authorized faces in the registered user database. If there’s no match, the system identifies the person as an intruder and promptly sends an alert to the designated authority. To enhance accessibility, a user-friendly graphical interface (GUI) has been developed using Python’s Tkinter.

Keywords : Computer Vision, Machine Learning, Deep Learning, Human Intrusion Detection, Face Recognition, Rule- based Notification, Real-time Monitoring, Surveillance System, Image Processing.

References :

1. Akula Surya Teja, Ginni Chandra Mohini, Dannana Dhanunjay, Dr. P M Manohar, ”Realtime Intrusion Detection System Using Open CV,” Journal of Survey in Fisheries Sciences, Visakhapatnam, India, 2023, pp. 2734-2740.
2. G. Mallikharjuna Rao, Haseena Palle, Pragna Dasari, Shivani Jan- naikode, Dr. P M Manohar, ”Implementation of Low Cost IoT Based Intruder Detection System by Face Recognition using Machine Learn- ing,” Turkish Journal of Computer and Mathematics Education, Vol. 12, No. 13, 2021, pp. 353-362.
3. K. S. Krishnendu, ”Analysis of Recent Trends in Face Recognition Systems,” 2023, arXiv:2304.11725.
4. Rehmat Ullah et al., ”A Real-Time Framework for Human Face De- tection and Recognition in CCTV Images,” Mathematical Problems in Engineering, 2022, Hindawi.
5. Edwin Jose, Greeshma M, Mithun Haridas T. P, ”Face Recognition based Surveillance System Using FaceNet and MTCNN on Jetson TX2,” in Proceedings of the 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019.
6. Philip Smith, Cuixian Chen, ”Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation,” 2018, arXiv:1811.07344v1 [cs.CV], 2018.
7. Franz Cardoz et al., ”Visual Sentinel: Data Analytics for Missing Subject Identification,” in Proceedings of the 2023 Pune International Conference, Pune, India, 2023.
8. Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Yu Qiao, ”Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Net- works,” IEEE SIGNAL PROCESSING LETTERS, October 10, 2016.
9. F. Schroff, D. Kalenichenko, J. Philbin, ”FaceNet: A unified embed- ding for face recognition and clustering,” in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 815-823. doi: 10.1109/CVPR.2015.7298682.
10. T. Sanjay, W. Deva Priya, ”Efficient System for Criminal Face De- tection Technique on Innovative Facial Features To Improve Accu- racy Using LBPH In Comparison With CNN,” Journal of Pharma- ceutical Negative Results, Volume 13, Special Issue 4, 2022. DOI: 10.47750/pnr.2022.13.S03.085.
11. Arnab Pushilal, Sulakshana Chakraborty, Raunak Singhania, P. Ma- halakshmi, ”Implementation of Facial Recognition for Home Security Systems,” International Journal of Engineering & Technology, Vol. 7, No. 4.10, 2018, pp. 55-58.
12. Mfundo Zuma et al., ”Intrusion Detection System using Raspberry Pi and Telegram Integration,” in Proceedings of icARTi ’21, 2021.
13. R. Prakash, P. Chithaluru, ”Active Security by Implementing Intrusion Detection and Facial Recognition,” in Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering, vol 692, Springer, Singapore, 2021.
14. Bazama A, Mansur F, Alsharef N. ”Security System by Face Recogni- tion,” Alq J Med App Sci, 2021;4(2):58-67.
15. Kajenthani Kanthaseelan et al., ”CCTV Intelligent Surveillance on Intruder Detection,” International Journal of Computer Applications, Volume 174, Issue 14, 2021.
16. R. Menaka et al., ”Enhanced Missing Object Detection System using YOLO,” in Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coim- batore, India, 2020.
17. J. Deng et al., ”ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” in Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 4685-4694.