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Facial Recognition to Identify Emotions: An Application of Deep Learning

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Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 72))

Abstract

Deep learning is an approach that is not recent. But its use in the field of emotion recognition is a very important and very recent subject. Because of its power in classification. In this work we used convolutional neural networks for based emotions recognition. (joy, sadness, anger, disgust, surprise, fear and neutral). Our proposed work is an intelligent system of emotion recognition with mathematical foundations explanation of convolutional neural networks. To evaluate our recognition system we used two evaluation metrics which are: The rate of good classification (tbcs) and Error rate. The recognition rate achieved is very satisfactory. Indeed our recognition system was able to recognize almost more than 90% of emotions.

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References

  1. Dachapally, P.R.: School of Informatics and Computing: Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units

    Google Scholar 

  2. Raghuvanshi, A., Choksi, V.: Facial Expression Recognition with Convolutional Neural Network, CS231n Course Projects Winter, (2016)

    Google Scholar 

  3. Xie, S., Hu, H.: Facial expression recognition with FRR – CNN. Electron. Lett. 53 (4), 235–237 (2017)

    Google Scholar 

  4. Jaiswal, S., Nandi, G.C: Robust real-time emotion detection system using CNN architecture. Neural Comput. Appl. 32, 11253–11262 (2020)

    Google Scholar 

  5. Lopesa, A.T., AguiarbAlberto, E., De Souzaa, F., Oliveira Santos, T.: Facial expression recognition with convolutional neural networks coping with few data and the training sample order. Pattern Recogn. 61, 610–628 (2017)

    Google Scholar 

  6. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(7), 21212159 (2011)

    Google Scholar 

  7. Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, Ng, A.Y: Large scale distributed deep networks. Adv. Neural Inf. Process. Syst. 1223–1231 (2012)

    Google Scholar 

  8. Clark, L. Google’s artificial brain learns to find cat videos. Wired UK, www. wired. (2012)

    Google Scholar 

  9. Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 46–53 (2000)

    Google Scholar 

  10. http://mmifacedb.eu/

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Belhouchette, K. (2021). Facial Recognition to Identify Emotions: An Application of Deep Learning. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_46

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