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.
CITATION STYLE
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 Advances in Intelligent Systems and Computing (Vol. 1239 AISC, pp. 177–185). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58356-9_18
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