Open Access
Research Article
AWS-rekognition-driven attendance system : Enhancing accuracy and security through cloud-based facial authentication
*Jyostna Devi BodapatiCorresponding authorjyostna.bodapati82@gmail.comDepartment of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and ResearchVadlamudi, Andhra Pradesh, India0000−0002−5185−882XView full profile → , Koduru Jagrutha Adithyakja8586@gmail.comDepartment of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and ResearchVadlamudi, Andhra Pradesh, India0009−0000−6368−7792View full profile → , Krishnaiah NarukullaKrisnaru@ieee.orgIEEECalifornia, U.S.A.0009−0006−5602−1021View full profile → , Santhosh Kumar Kuchoorsantosh.kuchoor@ieee.orgIEEECalifornia, U.S.A.0009−0006−5503−3258View full profile → , Suresh Bysanisureshbysani@ieee.orgIEEECalifornia, U.S.A.0009−0009−7691−2659View full profile → , N. Veeranjaneyuluveeru2006n@gmail.comDepartment of Information Technology Vignan’s Foundation for Science Technology and ResearchVadlamudi, Andhra Pradesh, India0000-0002-9331-2817View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 13 Aug 2024
- Published Online:
- 01 Jan 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1868
- Pages:
- 243–252
Abstract
Facial recognition technology is widely used in attendance management systems. This study develops an automated attendance sys- tem using AWS Rekognition, streamlining attendance tracking by eliminating manual record-keeping and reducing errors. The system comprises two key components: user registration and attendance marking. Users register their facial data through AWS Rekognition, requiring administrative approval for verification. Attendance is marked by facial au- thentication, with options for deregistration if needed. Role-based access control allows employees to view their records while administrator monitor all attendance data. By integrating AWS Rekognition, the system enhances accuracy, security, and efficiency, demonstrating the potential of cloud-based computer vision in real-world applications.
Keywords
Subject Classifications
68T07
References
[1] R. K. Kodali, A. Panda, and L. Boppana, ”Attendance System using Amazon Rekognition,” in TENCON 2023 - IEEE Region 10 Conference, pp. 65–70 (2023). doi: 10.1109/TENCON58879.2023.10322521.[2] M. Arsenovic, S. Sladojevic, A. Anderla, and D. Stefanovic, “FaceTime — Deep learning based face recognition attendance system,” in 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), pp. 53–58 (2017), doi: 10.1109/SISY.2017.8080587.[3] A. Mittal, F. S. Khan, P. Kumar, and T. Choudhury, “Cloud based intelligent attendance system through video streaming,” in 2017 International Conference on Smart Technologies for Smart Nation (SmartTechCon), pp. 1352–1357 (2017), doi: 10.1109/SmartTechCon.2017.8358587.[4] N. K. Jayant and S. Borra, “Attendance management system using hybrid face recognition techniques,” in 2016 Conference on Advances in Signal Processing (CASP), pp. 412–417 (2016), doi: 10.1109/CASP.2016.7746206.[5] S. Sharma, S. L. Shimi, and S. Chatterji, “Radio frequency identifiation (RFID) based employee monitoring system (EMS),” International Journal of Current Engineering and Technology, vol. 4, no. 5, pp. 3441–3444 (2014).[6] B. Soewito, F. L. Gaol, E. Simanjuntak, and F. E. Gunawan, “Smart mobile attendance system using voice recognition and fingerprint on smartphone,” in 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 175–180 (2016).[7] J. D. Bodapati, “A deep convolutional neural network framework for enhancing brain tumor diagnosis on MRI scans,” Information Dynamics and Applications, vol. 2, no. 1, pp. 42–50 (2023), doi: 10.56578/ida020105.[8] R. Sajja, S. F. Ahmed, J. D. Bodapati, and L. K. Yenduri, “Enhancing air quality prediction using machine learning algorithms and boosting techniques,” in Advances in AI for Biomedical Instrumentation, Electronics and Computing, CRC Press, pp. 7–12 (2024).[9] S. K. F. Ahmed et al., “Ensemble of diverse pre-trained convolutional neural networks for enhanced diabetic retinopathy image classification,” in Advances in AI for Biomedical Instrumentation, Electronics and Computing, CRC Press, pp. 1–6 (2024).[10] N. S. Shaik, T. K. Cherukuri, N. Veeranjaneulu, and J. D. Bodapati, “Medtransnet: Advanced gating transformer network for medical image classification,” Machine Vision and Applications, vol. 35, no. 4, p. 73 (2024).[11] S. Kamepalli, N. Veeranjaneyulu, and J. D. Bodapati, “Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery,” in 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), IEEE (2024).[12] J. D. Bodapati, “Adaptive ensembling of multi-modal deep spatial representations for diabetic retinopathy diagnosis,” Multimedia Tools and Applications, pp. 1–20 (2024).[13] Q. A. Al-Haija and S. O. Al-Salameen, “Biometric Authentication System on Mobile Environment: A Review,” Computer Systems Science & Engineering, vol. 48, no. 4 (2024).[14] A. F. A. Fatah, R. Mohamad, and F. Y. A. Rahman, “Student attendance system using an android based mobile application,” in 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE (2021).[15] M. Tamboli, A. T. S. Rao, A. M. Shah, R. S. Chinnapamma, and V. R. Shetty, “Attendance Management System Using Geofencing Technology,” in 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), IEEE (2024).[16] A. El-Mawla, M. Ismaiel, and A. S. Q. R. Team, “Smart Attendance System Using QR-Code, Finger Print and Face Recognition,” Nile Journal of Communication and Computer Science, vol. 2, no. 1, pp. 1–16 (2022).[17] P. Mohan, M. Tiwari, R. Patel, and A. Kumar, “Optimizing Time and Attendance Tracking Using Machine Learning,” International Journal of Research in Modern Engineering and Emerging Technology, vol. 12, no. 7, pp. 1–14 (2022).[18] K. Sujatha, P. Srinivas, M. Venkata, and A. P. Reddy, “Wearable Technology with Location Tracking, Health Monitoring, and Attendance Tracking for Employees,” in International Conference on Smart Trends in Computing and Communications, Singapore: Springer Nature Singapore (2023).[19] S. K. Geetha, S. Kanakaprabha, A. Devipriya, D. Brindha, and E. Vetrimani, “Implementation of a Blockchain Based Attendance Tracking System,” in 2023 International Conference on Computer Communication and Informatics (ICCCI), IEEE, pp. 1–5 (2023).
Views: 261Downloads: 9Citations: 0
