Abstract
In this paper, a touch less automated face recognition system for smart attendance application was designed using convolutional neural network (CNN). The presented touch less smart attendance system is useful for offices and college’s attendance applications with this the spread of covid-19 type viruses can be restrict. The CNN was trained with dedicated database of 1890 faces with different illumination levels and rotate angles of total 30 targeted classes. A CNN performance analysis was done with 9-layer and 11-layer with different activation functions i.e., Step, Sigmoid, Tanh, softmax, and ReLu. An 11-layer CNN with ReLu activation function offers an accuracy of 96.2% for the designed face database. The system is capable to detect multiple faces from test images using Viola Jones algorithm. Eventually, a web application was designed which helps to monitor the attendance and to generate the report.
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Thalluri, L.N., Babburu, K., Madam, A.K. et al. Automated face recognition system for smart attendance application using convolutional neural networks. Int J Intell Robot Appl 8, 162–178 (2024). https://doi.org/10.1007/s41315-023-00310-1
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DOI: https://doi.org/10.1007/s41315-023-00310-1