International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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A Study on the Facial Expression Recognition using Deep Learning Technique

  • Received : 2018.02.15
  • Accepted : 2018.03.10
  • Published : 2018.03.30
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Abstract

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the symbols that users often use, you can identify facial expressions with a camera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar expressions, reached 66%. It doesn't need to search for symbols. If you use the camera to recognize the expression, it will appear symbols immediately. So, this service is the symbols used when people send messages to others, and it can feel a lot of convenience. In countless symbols, there is no need to find symbols, which is an increasing trend in deep learning. So, we need to use more suitable algorithm for expression recognition, and then improve accuracy.

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References

  1. Boyle, G. J. (1998). Review of Arousal Seeking Tendency Scale. In J. C. Impara & B. S. Plake (Eds.), The thirteenth mental measurements yearbook (pp. 49-50). Lincoln, NE: Buros Institute of Mental Measurements.
  2. Darwin, C. (1871). The descent of man, and selection in relation to sex. London: John Murray.
  3. Ekman, P., & Friesen, W. V. (1977). The Facial Action Coding System (FACS): A Technique for the Measurement of Facial Action. Palo Alto: Consulting Psychologists Press.
  4. Mehrabian, A. (1971). Silent Messages (1st ed.). Belmont, CA: Wadsworth.
  5. NAMU (2017). Tenser Flow. Retrieved May 22, 2017. from https://namu.wiki/w/tenserflow
  6. Pervaiz, A. Z. (2010). Real Time Face Recognition System Based on EBGM Framework. In Computer Modelling and Simulation (UKSim),12th International Conference on 2010, pp.262-266.
  7. Rowley, H. A. Baluja, S., & Kanade, T. (1995). Human face detection in visual scenes. CMUCS-95-158R, Carnegie Mellon University, Retrieved November 22, 1995. from http://www.cs.cmu.edu/-har/faces.html.
  8. Wikipedia (2017a). Convolutional Neural Network. Retrieved May 22, 2017. from https://ko.wikipedia.org/wiki/%EB%94%A5_%EB%9F%AC%EB%8B%9D#.ED.95.A9.EC.84.B1.EA.B3.B1_.EC.8B.A0.EA.B2.BD.EB.A7.9D.28Convolutional_Neural_Network.2C_CNN.29
  9. Wikipedia (2017b). Deep Learning. Retrieved May 22, 2017. from https://ko.wikipedia.org/wiki/딥_러닝
  10. Wikipedia (2017c). Deep Learning. Retrieved May 22, from https://ko.wikipedia.org/wiki/%EC%96%BC% EA%B5%B4_%EA%B2%80%EC%B6%9C
  11. http://cs231n.stanford.edu/reports/2016/pdfs/022_Report.pdf
  12. http://aikorea.org/cs231n/convolutional-networks/#norm
  13. Gil Levi, Tal Hassner Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
  14. Y. Rubner, C. Tomasi, and L. J. Guibas. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vision, 40(2):99-121, 2000. https://doi.org/10.1023/A:1026543900054
  15. M. J. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba, and J. Budynek. The japanese female facial expression (jaffe) database, 1998.
  16. I. Borg and P. J. Groenen. Modern multidimensional scaling: Theory and applications. Springer Science & Business Media, 2005.
  17. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531, 2014.