Integrated Smart Gate for Vehicles and Pedestrian
Keywords:
Smart Gate, Face Detection, Access Control, Security, AutomationAbstract
Traditional authentication methods like manual verification or keycard access are often cumbersome and prone to errors. To address these challenges, a Smart Gate for Vehicles and Pedestrians Using Face Detection has emerged as a viable solution that uses advanced technologies for seamless access control. This system integrates face detection technology with automatic gates for both vehicles and pedestrians, ensuring efficient and secure entry and exit to restricted areas. Face detection is a reliable and nonintrusive method for identification, eliminating the need for physical tokens or credentials. The system enhances convenience and security by automating the verification process. The integration of machine learning algorithms allows for continuous improvement in accuracy. This paper discusses the design, implementation, and potential impact of a Smart Gate System powered by face detection.
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Divekar, S., & Nigam, M. K. (2022). Minimize Frequency Overlapping of Auditory Signals using Complementary Comb Filters. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 14(3), 333–336.
Divekar, S. N., Shinde, A. A., Mulay, R. R., & Jaybhaye, P.V. (2020). Real Time Bridge Monitoring System. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), 7(3), 406–411. [Online ISSN: 2394-4099, Print ISSN: 2395-1990]. Available at: http://ijsrset.com/IJSRSET2073100
Raza, H. W. (2022). IoT–Based Automatic Attendance Management System using Middleware.Kadepurkar, P., Dsouza, P. R., & Jomichan, N. (2021). IoT Based Smart Classroom. International Journal of Applied Sciences and Smart Technologies, 3(1), 35–54.
Shoewu, O. O., et al. (2020). Enhanced Smart Biometric Based Attendance (ES2BASYS) System Interfaced with POS Facility for a Smart Academic Institution. The Pacific Journal of Science and Technology, 21(2), 1–12.
Jain, T., et al. (2020). IoT Based Biometric Attendance System. International Journal of Electrical Engineering & Technology, 11(2).
Gore, N. S., et al. (2019). Fingerprint Based Attendance System using IoT. International Journal of Computer Science Trends and Technology (IJCST), 7(2), 64–68.
Panditpautra, V., et al. (2019). Biometric Attendance Management System Using Raspberry Pi. 2nd International Conference on Advances in Science & Technology (ICAST).
Zamkah, A., et al. (2020). Identification of Suitable Biomarkers for Stress and Emotion Detection for Future Personal Affective Wearable Sensors. Biosensors, 10(4), 40.
Akhmedov, F., et al. (2022). Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces. Sensors, 22(22), 8704.
Khaireddin, Y., & Chen, Z. (2021). Facial Emotion Recognition: State of the Art Performance on FER2013. arXiv preprint arXiv:2105.03588.
Zhang, H., Jolfaei, A., & Alazab, M. (2019). A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing. IEEE Access, 7, 159081–159089.
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