Abstract
This paper focuses on implementing face detection, face recognition and face emotion recognition through NVIDIA’s state-of-the-art Jetson Nano. Face detection is implemented using OpenCV’s deep learning-based DNN face detector, supported by a ResNet architecture, for achieving better accuracy than the previously developed models. The result computed by framework libraries of OpenCV, with the support of the above-mentioned hardware, displayed reliable accuracy even with the change in lighting and angle. For face recognition, the approach of deep metric learning using OpenCV, supported by a ResNet-34 architecture, is used. Face emotion recognition is achieved by developing a system in which the areas of eyes and mouth are used to convey the analysis of the information into a merged new image, classifying the image into displaying any of the seven basic facial emotions. A powerful and a low-power platform, Jetson Nano carried out intensive computations of algorithms easily, contributing in high video processing frame.
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Acknowledgments
This work was supported by the Spanish Junta de Castilla y León, Consejería de empleo. Project: UPPER, aUgmented reality and smart personal protective equipment (PPE) for intelligent pRevention of occupational hazards and accessibility INVESTUN/18/SA/0001.
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Sati, V., Sánchez, S.M., Shoeibi, N., Arora, A., Corchado, J.M. (2021). Face Detection and Recognition, Face Emotion Recognition Through NVIDIA Jetson Nano. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications. ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_18
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DOI: https://doi.org/10.1007/978-3-030-58356-9_18
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