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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References
Bledsoe, W.W.: The model method in facial recognition, vol. 15, no. 47, p. 2. Panoramic Research Inc., Palo Alto, CA, Report PR1 (1966)
Chan, H., Bledsoe, W.W.: A man-machine facial recognition system: some preliminary results. Panoramic Research Inc., Palo Alto, CA, USA (1965)
Bledsoe, W.W.: Some results on multicategory pattern recognition. J. ACM (JACM) 13(2), 304–316 (1966)
Bledsoe, W.W.: Semiautomatic Facial Recognition. Stanford Research Institute, Menlo Park, CA, USA (1968)
Face Recognition. http://www.face-rec.org/algorithms/
Wikipedia, Three-Dimensional Face Recognition. http://en.wikipedia.org/wiki/Threedimensional_face_recognition
Wikipedia, Active Appearance Model. http://en.wikipedia.org/wiki/Active_appearance_model
Computer Vision Papers. http://www.cvpapers.com/
Swain, M., Routray, A., Kabisatpathy, P.: Databases, features and classifiers for speech emotion recognition: a review. Int. J. Speech Technol. 21(1), 93–120 (2018)
Kołakowska, A.: A review of emotion recognition methods based on keystroke dynamics and mouse movements. In: 2013 6th International Conference on Human System Interactions (HSI), pp. 548–555. IEEE, June 2013
Ko, B.C.: A brief review of facial emotion recognition based on visual information. Sensors 18(2), 401 (2018)
Ghayoumi, M.: A quick review of deep learning in facial expression. J. Commun. Comput. 14(1), 34–8 (2017)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE, May 2013
Mustafa, M.B., Yusoof, M.A., Don, Z.M., Malekzadeh, M.: Speech emotion recognition research: an analysis of research focus. Int. J. Speech Technol. 21(1), 137–156 (2018)
Huang, K.Y., Wu, C.H., Yang, T.H., Su, M.H., Chou, J.H.: Speech emotion recognition using autoencoder bottleneck features and LSTM. In: 2016 International Conference on Orange Technologies (ICOT), pp. 1–4. IEEE, December 2016
Le, D., Provost, E.M.: Emotion recognition from spontaneous speech using hidden Markov models with deep belief networks. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 216–221. IEEE, December 2013
Harár, P., Burget, R., Dutta, M.K.: Speech emotion recognition with deep learning. In: 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 137–140. IEEE, February 2017
Mazzia, V., Khaliq, A., Salvetti, F., Chiaberge, M.: Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application. IEEE Access 8, 9102–9114 (2020)
Srinivasa, S.S., Lancaster, P., Michalove, J., Schmittle, M., Rockett, C.S.M., Smith, J. R., Choudhury, S., Mavrogiannis, C., Sadeghi, F.: MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Research. arXiv preprint arXiv:1908.08031 (2019)
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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