ATTENDANCE SYSTEM USING FACE RECOGNITION AND RASPBERRY PI – REVIEW
Keywords:
Attendance System, Raspberry Pi, Facial Recognition, OpenCV, PythonAbstract
The current attendance procedure is manual. Both professors and students lose a lot of time as a result. Wait times for students increase when attendance is manually recorded. Human mistake is closely linked to manual labor. Every person's face serves as important, recognizable proof. Therefore, automating the attendance procedure will increase learning time in the classroom. To make facial recognition accessible to everyone, we picked the Raspberry Pi. A Raspberry Pi-based facial recognition system using traditional facial recognition and recognition mechanisms is provided. For security and surveillance purposes, facial recognition is crucial. Therefore, a system that is effective and affordable is needed. Face recognition is the foundation of the identification process, which is then broken down into the following three steps: face recognition, feature extraction and classification, and real-time detection. It is acknowledged that facial recognition is a crucial stage in our system. This system is implemented in Python using the OpenCV library.
References
Zobaed SM, Hassan M, Islam MU, Haque ME. Deep learning in IOT-based healthcare applications. InDeep Learning for Internet of Things Infrastructure 2021 Sep 30 (pp. 183-200). CRC Press.
Kumar A, Salau AO, Gupta S, Paliwal K. Recent trends in IoT and its requisition with IoT built engineering: a review. Advances in Signal Processing and Communication. 2019:15-25.
Ahmad R, Alsmadi I. Machine learning approaches to IoT security: A systematic literature review. Internet of Things. 2021 Jun 1;14:100365.
Harbi Y, Aliouat Z, Refoufi A, Harous S. Recent security trends in internet of things: A comprehensive survey. IEEE Access. 2021 Aug 9.
Jabbar WA, Wei CW, Azmi NA, Haironnazli NA. An IoT Raspberry Pi-based parking management system for smart campus. Internet of Things. 2021 Jun 1;14:100387.
Zhao CW, Jegatheesan J, Loon SC. Exploring iot application using raspberry pi. International Journal of Computer Networks and Applications. 2015 Jan;2(1):27-34.
Zamir M, Ali N, Naseem A, Ahmed Frasteen A, Zafar B, Assam M, Othman M, Attia EA. Face Detection & Recognition from Images & Videos Based on CNN & Raspberry Pi. Computation. 2022 Aug 30;10(9):148.
Gupta I, Patil V, Kadam C, Dumbre S. Face detection and recognition using Raspberry Pi. In2016 IEEE international WIE conference on electrical and computer engineering (WIECON-ECE) 2016 Dec 19 (pp. 83-86). IEEE.
Wazwaz AA, Herbawi AO, Teeti MJ, Hmeed SY. Raspberry Pi and computers-based face detection and recognition system. In2018 4th International Conference on Computer and Technology Applications (ICCTA) 2018 May 3 (pp. 171-174). IEEE.
Singh S, Ramya R, Sushma V, Roshini SR, Pavithra R. Facial recognition using machine learning algorithms on raspberry Pi. In2019 4th Internaonal Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT) 2019 Dec 13 (pp. 197-202). IEEE.
Rahmad, C., Asmara, R. A., Putra, D. R. H., Dharma, I., Darmono, H., & Muhiqqin, I. (2020). Comparison of Viola-Jones Haar Cascade classifier and histogram of oriented gradients (HOG) for face detection. In IOP conference series: materials science and engineering (Vol. 732, No. 1, p. 012038). IOP Publishing.
Shrivastava, K., Manda, S., Chavan, P. S., Patil, T. B., & Sawant-Patil, S. T. (2018). Conceptual model for proficient automated attendance system based on face recognition and gender classification using Haar-Cascade, LBPH algorithm along with LDA model. International Journal of Applied Engineering Research, 13(10), 8075-8080.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Ismail Mujahid Mukadam, Ansari Mohammed Sajjad , Shaikh Fuzail Shahnawaz, Prof. Salim G. Shaikh (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
