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Facial Expression Recognition System (FERS): A Survey

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Intelligent and Cloud Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 153))

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Abstract

Human facial expressions and emotions are considered as the fastest way of the communication medium for expressing thoughts. The ability to identify the emotional states of people surrounding us is an essential component of natural communication. Facial expression and emotion detector can be used to know whether a person is sad, happy, angry, and so on. We can better understand the thoughts and ideas of a person. This paper briefly explores the idea of recognizing the computerized facial expression detection system. First, we have discussed an overview of the facial expression recognition system (FERS). Also, we have presented a glimpse of current technologies that are used for the detection of FERS. A comparative analysis of existing methodologies is also presented in this paper. It provides the basic information and general understanding of up-to-date state-of-the-art studies; also, experienced researchers can look productive directions for future work.

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References

  1. Dong, J., Zheng, H., Lian, L.: Dynamic facial expression recognition based on convolutional neural networks with dense connections. In: 24th IEEE International Conference on Pattern Recognition (ICPR), pp. 3433–3438 (2018)

    Google Scholar 

  2. Girard, J.M., Cohn, J.F., Mahoor, M.H., Mavadati, S.M., Hammal, Z., Rosenwald, D.P.: Nonverbal social withdrawal in depression: evidence from manual and automatic analyses. Image Vis. Comput. 32(10), 641–647 (2014)

    Article  Google Scholar 

  3. McDaniel, B., D’Mello, S., King, B., Chipman, P., Tapp, K., Graesser, A.: Facial features for affective state detection in learning environments. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 29 (2007)

    Google Scholar 

  4. Assari, M.A., Rahmati, M.: Driver drowsiness detection using face expression recognition. In: IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 337–341 (2011)

    Google Scholar 

  5. Mishra, S., Majhi, B., Sa, P.K.: A survey on automated diagnosis on the detection of Leukemia: a hematological disorder. In: 3rd IEEE International Conference on Recent Advances in Information Technology (RAIT), pp. 460-466 (2016)

    Google Scholar 

  6. Pan, X., Ying, G., Chen, G., Li, H., Li, W.: A deep spatial and temporal aggregation framework for video-based facial expression recognition. IEEE Access 7, 48807–48815 (2019)

    Article  Google Scholar 

  7. Minaee, S., Abdolrashidi, A.: Deep-emotion: facial expression recognition using attentional convolutional network. arXiv:1902.01019 (2019)

  8. Mokhayeri, F., Granger, E.: Robust video face recognition from a single still using a synthetic plus variational model. In: 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1–8 (2019)

    Google Scholar 

  9. Albrici, T., Fasounaki, M., Salimi, S.B., Vray, G., Bozorgtabar, B., Ekenel, H.K., Thiran, J.P.: G2-VER: geometry guided model ensemble for video-based facial expression recognition. In: 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1–6 (2019)

    Google Scholar 

  10. Zhang, S., Pan, X., Cui, Y., Zhao, X., Liu, L.: Learning affective video features for facial expression recognition via hybrid deep learning. IEEE Access 7, 32297–32304 (2019)

    Article  Google Scholar 

  11. Liu, C., Tang, T., Lv, K., Wang, M.: Multi-feature based emotion recognition for video clips. In: Proceedings of the 2018 on International Conference on Multimodal Interaction, pp. 630–634. ACM (2018)

    Google Scholar 

  12. Xu, Q., Sun, B., He, J., Rong, B., Yu, L., Rao, P.: Multimodal facial expression recognition based on Dempster-Shafer theory fusion strategy. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), pp. 1–5. IEEE (2018)

    Google Scholar 

  13. Zeng, N., Zhang, H., Song, B., Liu, W., Li, Y., Dobaie, A.M.: Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273, 643–649 (2018)

    Article  Google Scholar 

  14. Sun, A., Li, Y., Huang, Y.M., Li, Q., Lu, G.: Facial expression recognition using optimized active regions. Human-Centric Comput Inf Sci 8(1), 33 (2018)

    Article  Google Scholar 

  15. Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: Proceedings 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)

    Google Scholar 

  16. Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.H., Zhou, Y.: Challenges in representation learning: A report on three machine learning contests. In: International Conference on Neural Information Processing, pp. 117–124. Springer, Berlin, Heidelberg (2013)

    Google Scholar 

  17. Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H., Hawk, S.T., Van Knippenberg, A.D.: Presentation and validation of the Radboud faces database. Cognit. Emotion 24(8), 1377–1388 (2010)

    Article  Google Scholar 

  18. Mollahosseini, A., Hasani, B., Mahoor, M.H.: Affectnet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18–31 (2017)

    Article  Google Scholar 

  19. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 94–101 (2010)

    Google Scholar 

  20. Dhall, A., Ramana Murthy, O.V., Goecke, R., Joshi, J., Gedeon, T.: Video and image based emotion recognition challenges in the wild: Emotiw 2015. In: Proceedings of the ACM on International Conference on Multimodal Interaction, pp. 423–426 (2015)

    Google Scholar 

  21. Zhao, G., Huang, X., Taini, M., Li, S.Z., PietikInen, M.: Facial expression recognition from near-infrared videos. Image Vis. Comput. 29(9), 607–619 (2011)

    Article  Google Scholar 

  22. Porcu, S., Uhrig, S., Voigt-Antons, J.N., Mller, S., Atzori, L.: Emotional impact of video quality: self-assessment and facial expression recognition. In: IEEE 11th International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6 (2019)

    Google Scholar 

  23. Song, X., Bao, H.: Facial expression recognition based on video. In: IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–5 (2016)

    Google Scholar 

  24. Shan, K., Guo, J., You, W., Lu, D., Bie, R.: Automatic facial expression recognition based on a deep convolutional-neural-network structure. In: IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 123–128 (2017)

    Google Scholar 

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Correspondence to Sonali Mishra .

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Mishra, S., Gupta, R., Mishra, S.K. (2021). Facial Expression Recognition System (FERS): A Survey. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 153. Springer, Singapore. https://doi.org/10.1007/978-981-15-6202-0_5

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