Abstract
Today we are in an era of feasible biometric solutions to the age-old problem of verifying personal identity. As a form of identity that is verified through inseparable and unique characteristics of a person: their face - facial recognition is now one of the most popular methods in use. We propose an automated real-time facial attendance system where the users can verify their identity without physical contact with any surface. This is achieved through a conjunction of a mobile and platform-independent web application over a shared cloud database. The system is powered by a Face Recognition module to authenticate the users and demonstrated 99.7% of test accuracy, with an improved true positivity rate of 96.14% compared to some existing literature. Our system processes and aligns input face images before utilizing a deep convolutional neural network model to recognize the user’s identity. To test the efficacy of our system, we have built a database of over 800 unique individuals of Indian descent. Our tests showed a superior true-positivity rate on our tweaked model demonstrating its efficacy in comparison with the reference literature. We also share some of the methods employed to raise system redundancy and minimize false negativity; a vital metric in any authentication application.
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Bavikadi, D. et al. (2022). Real-Time Face Recognition for Organisational Attendance Systems. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_13
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DOI: https://doi.org/10.1007/978-3-031-07005-1_13
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