Abstract
Recognition of faces is of great importance for applications in the real world such as securities, human–computer interfaces, and traffic. As opposed to conventional machine learning methods, neural network models have also demonstrated improved outcomes in the case of processing precision in face detection. Surveillance system provides a close examination of a region, or the suspect in specific. The paper implements a novel system with the Internet of Things (IoT) capabilities for real-time surveillance using Haar-cascade detection integrated with a convolutional neural network for recognizing facial attributes to identify and allow access. The system presents 96.3% accuracy with real-time response through SMS or mail.
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Gondane, S., Thakare, A., Deshpande, C., Gupta, O. (2021). A Convolution Neural Networks and IoT-Based Approach to Surveillance System. In: Choudhury, S., Gowri, R., Sena Paul, B., Do, DT. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 1341. Springer, Singapore. https://doi.org/10.1007/978-981-16-1510-8_3
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DOI: https://doi.org/10.1007/978-981-16-1510-8_3
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