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Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement

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Proceedings of International Conference on Trends in Computational and Cognitive Engineering

Abstract

Face detection and recognition is a prevalent concept in security and access control area which is commonly used in surveillance cameras at public places, attendance etc. But often this type of system can be circumvented by holding a photo or running a video of authorized person to the camera. Therefore, liveliness concept comes up with a solution to detect the person is real or spoofed. In this paper, We proposed a cascade classifier based model for detecting liveliness using deep-learning and Heart-beat measurement. Moreover, we have evaluated our model accuracy with our own dataset of real and fake videos and photos. By using our proposed model of face liveliness detection model, FPR and FNR have declined 16% and 5.22% respectively. In addition, we have also compared proposed system with other state-of-art methods. And here proposed study has achieved an accuracy of 99.46%.

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Correspondence to Md. Mahfujur Rahman .

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Rahman, M.M., Mamun, S.A., Kaiser, M.S., Islam, M.S., Rahman, M.A. (2021). Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_47

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