Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi
DOI:
https://doi.org/10.29304/jqcsm.2024.16.11438Keywords:
Face detection, Face recognition, Haar Cascade, Raspberry Pi, Machine learning, Deep learning, Local Binary Pattern attacksAbstract
The modern world is full of data of all kinds; however, the vast amount of video and image data available provides the data set needed for facial recognition. Facial recognition is crucial in safety and surveillance systems that analyze visual data and millions of images. Using facial recognition software, a person's identity can be verified through a variety of media images. For facial recognition, there is a variety of algorithms available. The article presents an approach to a face recognition framework using Haar Cascade, a biometric technology in safety and surveillance systems. It investigates combining standard machine learning techniques for face detection and identification with Raspberry Pi face detection, a cost-effective and easy-to-use embedded system. The system detects faces from indirect and direct images, achieving high speed using the latest Raspberry Pi 4 and Python libraries. The work demonstrates a machine-learning-based design method and a complete embedded system. The face detection accuracy is 92%, and the average time is 0.35 compared to the local binary model (LBP). Many facial recognition algorithms on the web and in literature reviews are vulnerable to image attacks. These methods are very effective in identifying faces in webcams, video streams, images, and videos. This system's use of the Raspberry Pi 4 and advanced Python libraries results in fast and accurate real-time face detection. This paper extends the work first presented at the 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP).
Downloads
References
Alhajim, D., Akbarizadeh, G. and Ansari-Asl, K., “FFDR: Design and implementation framework for face detection based on raspberry pi,” in 2022 International Conference on Machine Vision and Image Processing (MVIP) (pp. 1-4). IEEE, DOI: 10.1109/MVIP53647.2022.9738788..
Tabassum, S., & Hossain, "Design and Development of Weather Monitoring and Controlling System for a Smart Agro (Farm)," ICA, vol. 9, pp. 65, 2018. DOI: https://dx.doi.org/10.4236/ica.2018.93005.
C. Hegde, Z. Jiang, PB. Suresha, J. Zelko, S. Seyedi, M.Smith, ... & G. D. Clifford, " Autotriage-an open source edge computing raspberry pi-based clinical screening system, " medRxiv, 2020. DOI: https://doi.org/10.1101/2020.04.09.20059840.
S. Pallavi, M. S. Sannidhan, Abhir BhandaryRetrieval of Facial Sketches Using Linguistic Descriptors: An Approach Based on Hierarchical Classification of Facial Attributes, "In Advances in Artificial Intelligence and Data Engineering, pp. 1131-1149, Springer, Singapore, 2021.
DOI: https://doi.org/10.1007/978-981-15-3514-7_84.
Z. Jiang, S. Yang, H. Huang, X. He, Y. Kong, A. Gao, C. Liu, K. Yan, and
S. Wang, “ Programmable liquid crystal display based noise reduced dynamic synthetic coded aperture imaging camera (NoRDS-CAIC), " Optics express, vol. 28, pp. 5221-5238, 2020. DOI: https://dx.doi.org/10.1364/oe.385547
Mahalle, D. Ashlesha, “Artificial Intelligence Based Mask Detection With Thermal Scanning and Hand Sanitization Based Entry System, " Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12,
pp. 299-304, 2021.
Mayur Surve, Priya Joshi, Sujata Jamadar, Minakshi Vharkate, Automatic attendance system using face recognition technique, Int. J. Recent Technol. Eng. (IJRTE).IEEE 9 (1) (May 2020). DOI:
https://doi.org/10.1016/j.gltp.2021.08.044
[Online]. Available: https://www.turcomat.org/index.php/turkbilmat/article/view/8288/6464
Pajankar, Ashwin, " Introduction to Raspberry Pi, " In Practical Linux with Raspberry Pi OS,. In press, Berkeley, CA, pp. 1-34, 2021.
DOI: https://doi.org/10.1007/978-1-4842-6510-9_1.
Khairuddin, M. H., S. Shahbudin, and M. Kassim, "A smart building security system with intelligent face detection and recognition," IOP Conference Series: Materials Science and Engineering, Vol. 1176. No. 1. IOP Publishing, 2021. DOI:
https://doi.org/10.1088/1757-899x/1176/1/012030.
T.N. Nguyen, S. Zeadally, A. Vuduthala, Cyber-physical cloud manufacturing systems with digital-twins, IEEE Internet Comput. (2021). DOI:
https://doi.org/10.1109/MIC.2021.3058921.
J. Wang, , C. Jiang, H. Zhang, , Y. Ren, , K. C. Chen, , and L. Hanzo, " Thirty years of machine learning: The road to Pareto-optimal wireless networks, " IEEE Communications Surveys and Tutorials, vol. 22, pp. 1472-1514, 2020. DOI:
https://dx.doi.org/10.1109/comst.2020.2965856
H.Wechsler, "Reliable face recognition methods: system design, implementation and evaluation, " Springer Science and Business Media, vol. 7,2009.
DOI: https://doi.org/10.1007/978-0-387-38464-1_8.
Pavan, D, " REAL TIME OBJECT DETECTION USING DEEP LEARNING " , 2020. [Online].
Available: DOI:
http://ece.anits.edu.in/2019- 20%20BE%20Project%20REPORTS/CHPS%20_2019-
%20project%20batch_2.pdf.
Cerf, Moran, Jonathan Harel, Wolfgang Einhäuser, and Christof Koch, " Predicting human gaze using low-level saliency combined with face detection, " Advances in neural information processing systems, vol. 20,
pp. 1-7, 2008. [Online]. Available: DOI:
https://proceedings.neurips.cc/paper/2007/file/708f3cf8100d5e71834b1db 77dfa15d6-Paper.pdf.
C.Zhong, ,A. R. Reibman,H. A. Mina, , and A. J. Deering, " Designing a Computer-Vision Application: A Case Study for Hand-Hygiene Assessment in an Open-Room Environment, " Journal of Imaging, vol. 7,
pp. 70, 2021. DOI:
https://dx.doi.org/10.3390/jimaging7090170.
D. Wu, ,S. Chen, ,Y. Zhang, ,H. Zhang, ,Q. Wang,J. Li and Z. Shen, " Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition, " Journal of Personalized Medicine, vol. 11, pp. 1172,2021. DOI:
https://dx.doi.org/10.3390/jpm11111172.
D. Boczar,F. R. Avila,R. E. Carter,P. A. Moore,D. Giardi,G.Guliyeva and
A. J.Forte, " Using facial recognition tools for health assessment, "
Plastic Surgical Nursing, vol. 41, pp. 112-116, 2021. DOI: https://doi.org/10.1097/PSN.0000000000000410.
Shetty, Anirudha B., and Jeevan Rebeiro. "Facial recognition using Haar cascade and LBP classifiers." Global Transitions Proceedings 2.2 (2021): 330-335. DOI:
https://doi.org/10.1016/j.gltp.2021.08.044
N. Palanivel, S. Aswinkumar, J. Balaji, Automated attendance systems using face recognition by K-means algorithms, International Conference on System Computation Automation and Networking, IEEE, 2019.
DIO: https://doi.org/10.1109/ICSCAN.2019.8878790
D Jenif, W S Souza, S Jothi, A Chandrasekar, Automated attendance marking and management system by facial recognition using histogram, 5th International Conference on Advanced Computing & Communication Systems (ICACCS), IEEE, 2019. 334 [5][1]Fig.8Fig.9 A.B. Shetty,
Bhoomika, Deeksha et al. Global Transitions Proceedings 2 (2021) 330–
DOI: https://doi.org/10.1109/ICACCS.2019.8728399
AZM Ehtesham Chowdhury, Omar Khaium Chowdhury, Md.Assaduzzaman Samrat, Md.Zillur Rahman, Tanvir Ahmed, HFDCM: a low-cost machine learning based class attendance monitoring system, 2nd International Conference on Intelligent Comput ing, Instrumentation and Control Technologies (ICICICT), IEEE, 2019.
DOI:
https://doi.org/10.1109/ICICICT46008.2019.8993212.
R Nandhini, N Duraimurugan, S.P. Chokkalingam, Face recognition based attendance system, Int. J. Eng. Adv. Technol. (IJEAT) 8 (3S) (February 2019) ISSN: 2249–8958. DOI:
http://dx.doi.org/10.17577/IJERTV9IS060615
Omar Abdul Rhman Salim, Rashidah Funke Olanrewaju, Wasiu Adebayo Balogun, Class attendance management system using face recognition, 7th International Conference on Computer and Communication Engineering (ICCCE), IEEE, 2018. DOI: 10.1109/ICCCE.2018.8539274
X-Y Li, Xiang-Yu, Face recognition supported HOG and quick PCA rule, Euro China Conference on Intelligent Data Analysis and Applications, 2017. DOI:
https://doi.org/10.1016/j.gltp.2021.08.044
C.B. Yuvaraj, M. Srikanth, V. Santhosh Kumar, Y.V. Srinivasa Murthy, and Shashidhar G. Koolagudi, “An approach to maintain attendance using image processing techniques”, International Conference on Contemporary Computing (IC3), 10-12 August 2017,Noida, India.
DIO: https://ieeexplore.ieee.org/document/8284353
Sujay Patole, Yatin Vispute, Automatic attendance system based on face recognition, Int. J. Innov. Res. Sci., Eng. Technol. 6 (August 2017). DOI: https://www.semanticscholar.org/paper/Automatic-Attendance- System-Based-On-Face-Patole-
Vispute/159faa1d7b1fa687730fc84f55cf2a58001b320e
Viola, P. and Jones, M.J., 2004. "Robust real-time face detection,". International journal of computer vision, 57(2), pp.137-154.
DOI: https://doi.org/10.1023/B:VISI.0000013087.49260.fb
F.M. Javed Mehedi Shamrat, Anup Majumder, Probal Roy Antu, Saykot Kumar Barmon, ItishaNowrin, Rumesh Ranjan “Human Face Recognition Applying Haar Cascade Classifier” International Conference on Pervasive Computing and Social Networking, Salem, Tamil Nadu, India, 19-20, March 2021 DOI:
DOI: https://doi.org/10.1007/978-981-16-5640-8_12
Sirajdin Olagoke Adeshina, Haidi Ibrahim, Soo siang teoh and seng chun hoo “custom face classification model for classroom using haar-like and lbp features with their performance comparisons”, MDPI 6, Jan 2021. DOI:
DOI:https://doi.org/10.3390/electronics10020102
Sharma, Riti, and Andreas Savakis. "Lean histogram of oriented gradients features for effective eye detection." Journal of Electronic Imaging 24.6 (2015): 063007-063007.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Dhafer Alhajim , Hassoon Salman FAHAMA, Mohammed T.A
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
