Real Time Face-mask Detection with Arduino to Prevent COVID-19 Spreading
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Abstract
Abstract— The rise of COVID-19 pandemic has had a lasting impact in many countries worldwide since 2019. Face-mask detection had been significant progress in the Image processing and deep learning fields studies. Many face detection models have been designed using different algorithms and techniques. The proposed approach in this paper developed to avoid mask-less people from entering to a desired places (i.e. Mall, University, Office, …etc.) by detecting face mask using deep learning, TensorFlow, Keras, and OpenCV and sending a signal to Arduino device that connected to the gate to be open. it detect a face in a real-time and identifies whether the person wear mask or not. The method attains accuracy up to 97.80%. The dataset provided in this paper, was collected from various sources.
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References
Asaad, R., & Ali, R. (2019). Back Propagation Neural Network(BPNN) and Sigmoid Activation Function in Multi-Layer Networks.Academic Journal Of Nawroz University,8(4), 216. doi: 10.25007/ajnu.v8n4a464.[2] Zhang C., Zhang Z.A, (2010). Survey of Recent Advances in Face Detection.Microsoft Corporation; Albuquerque, NM, USA. TechReport, No. MSR-TR-2010-66.[3] 3.Ekman P., Friesen W., Hager J.(2002). Facial Action Coding System: The Manual on CD ROM.A Human Face; Salt Lake City, UT, USA.[4] Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatial-contextual information.European Journal of Remote Sensing,47(1), 389-411.[5] Kwon, O. W., Chan, K., Hao, J., & Lee, T. W. (2003). Emotion recognition by speech signals. InEighth European Conference on Speech Communication and Technology.[6] Schuller, B., Rigoll, G., & Lang, M. (2003, April). Hidden Markov model-based speech emotion recognition. In2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03).(Vol. 2, pp. II-1). IEEE.[7] El Ayadi, M., Kamel, M. S., & Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases.Pattern Recognition,44(3), 572-587.[8] Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction.IEEE Signal processing magazine,18(1), 32-80.[9] Nwe, T. L., Foo, S. W., & De Silva, L. C. (2003). Speech emotion recognition using hidden Markov models.Speech communication,41(4), 603-623.[10] Busso, C., Lee, S., & Narayanan, S. (2009). Analysis of emotionally salient aspects of fundamental frequency for emotion detection.IEEE transactions on audio, speech, and language processing,17(4), 582-596