Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Efficient Emotional Relaxation Framework with Anisotropic Features Based Dijkstra Algorithm

  • Received : 2020.03.23
  • Accepted : 2020.04.21
  • Published : 2020.04.29
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Abstract

In this paper, we propose an efficient emotional relaxation framework using Dijkstra algorithm based on anisotropic features. Emotional relaxation is as important as emotional analysis. This is a framework that can automatically alleviate the person's depression or loneliness. This is very important for HCI (Human-Computer Interaction). In this paper, 1) Emotion value changing from facial expression is calculated using Microsoft's Emotion API, 2) Using these differences in emotion values, we recognize abnormal feelings such as depression or loneliness. 3) Finally, emotional mesh based matching process considering the emotional histogram and anisotropic characteristics is proposed, which suggests emotional relaxation to the user. In this paper, we propose a system which can recognize the change of emotion easily by using face image and train personal emotion by emotion relaxation.

본 논문에서는 비등방성 특징 기반의 다익스트라 알고리즘(Dijkstra algorithm)을 이용한 효율적인 감성 완화 프레임워크를 제안한다. 감성을 완화시키는 것은 감성 분석만큼이나 중요하며, 사람의 우울함이나 외로움을 자동으로 완화시켜줄 수 있는 프레임워크로써 인간과 컴퓨터의 상호작용(HCI, Human-Computer Interaction)측면에서도 매우 중요한 의미를 갖는다. 본 논문에서는 1) 마이크로소프트의 Emotion API를 이용하여 얼굴 표정으로부터 변화하는 감정값을 계산하고, 2) 이 감정값의 차이를 이용하여 우울이나 외로움 같은 이상 감정을 인지한다. 3) 마지막으로, 감성 히스토그램과 비등방성 특성을 고려한 감정 메시 기반의 매칭 과정을 거침으로써 사용자에게 완화된 감성이 내포된 이미지들을 제시해준다. 본 논문에서 제안하는 기법은 얼굴 영상을 이용하여 사용자가 쉽게 감성의 변화를 인지하고, 완화된 감성으로 감정을 트레이닝 할 수 있는 시스템이다.

Keywords

References

  1. Jain, Neha, et al. "Hybrid deep neural networks for face emotion recognition." Pattern Recognition Letters, pp. 101-106, 2018. DOI:10.1016/j.patrec.2018.04.010
  2. Liu, Ping, Shizhong Han, Zibo Meng, and Yan Tong. "Facial expression recognition via a boosted deep belief network." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1805-1812, 2014. DOI:10.1109/CVPR.2014.233
  3. Parkhi, Omkar M., Andrea Vedaldi, and Andrew Zisserman. "Deep face recognition." The British Machine Vision Conference, Vol. 1, No. 3, pp. 6, 2015. DOI:10.5244/C.29.41
  4. Chen, Yang, Yu-Kun Lai, and Yong-Jin Liu. "Cartoongan: Generative adversarial networks for photo cartoonization." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9465-9474. 2018. DOI:10.1109/CVPR.2018.00986
  5. Geng, Jiahao, Tianjia Shao, Youyi Zheng, Yanlin Weng, and Kun Zhou. "Warp-guided GANs for single-photo facial animation." SIGGRAPH Asia, pp. 231, 2018. DOI:10.1145/3272127.3275043
  6. Ekman, Paul. "An argument for basic emotions." Cognition & Emotion, No. 3-4, pp. 169-200, 1992. DOI:10.1080/02699939208411068
  7. Vogt, Thurid, and Elisabeth Andre. "Improving Automatic Emotion Recognition from Speech via Gender Differentiaion." International Conference on Language Resources and Evaluation, pp. 1123-1126, 2006. DOI:10.1.1.380.6411
  8. Nakasone, Arturo, Helmut Prendinger, and Mitsuru Ishizuka. "Emotion recognition from electromyography and skin conductance." International Workshop on Biosignal Interpretation, pp. 219-222. 2005. DOI:10.1.1.104.4937
  9. NDimberg, U., "Facial electromyography and the experience of emotion". Journal of Psychophysiology, 1988. DOI:1989-28675-001
  10. Mase, K., "Recognition of facial expression from optical flow". IEICE Transactions on Information and Systems, Vol. 74, No. 10, pp. 3474-3483, 1991.
  11. Cohen, I., Sebe, N., Garg, A., Lew, M.S. and Huang, T.S., "Facial expression recognition from video sequences". International Conference on Multimedia and Expo, Vol. 2, pp. 121-124, 2002.
  12. Yeom, Hong-Gi, Jong-Tae Joo, and Kwee-Bo Sim. "Emotion Recognition and Expression System of User using Multi-Modal Sensor Fusion Algorithm." Journal of Korean Institute of Intelligent Systems, Vol. 18, No. 1 pp. 20-26, 2008. DOI:10.5391/JKIIS.2008.18.1.020
  13. Hancock, Peter JB, Roland J. Baddeley, and Leslie S. Smith. "The principal components of natural images." Network: Computation in Neural Systems, Vol. 3, No. 1, pp. 61-70, 1992. DOI:10.1088/0954-898X_3_1_008
  14. Lu, Juwei, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos. "Face recognition using LDA-based algorithms." IEEE Transactions on Neural Networks, Vol. 14, No. 1, pp. 195-200, 2003. DOI:10.1109/TNN.2002.806647
  15. Comon, Pierre. "Independent component analysis, a new concept?." Signal Processing, Vol. 36, No. 3, pp.287-314, 1994. DOI:10.1016/0165-1684(94)90029-9
  16. Del Sole, Alessandro. "Introducing microsoft cognitive services." In Microsoft Computer Vision APIs Distilled, pp. 1-4. Apress, Berkeley, CA, 2018. DOI:10.1007/978-1-4842-3342-9_1
  17. Eric Reinhard, Michael Ashikhmin, Bruce Gooch, Peter Shirley, "Color Transfer Between Images", Computer Graphics and Applications, Vol. 21, Issue 5, pp. 34-41, 2001. DOI:10.1109/38.946629
  18. Youngha Chang, Suguru Saito, Masayuki Nakajima, "Color transformation based on Basic Color Categories of a Painting", Proceedings of Computer Graphics International, pp. 176-181, 2003. DOI:10.1145/1242073.1242170
  19. B. Berlin, P. Kay, "Basic Color Terms: Their Universality and Evolution", University of California Press, 1969. DOI:10.1002/col.5080170514
  20. Donald H. House, Gary R. Greenfield, "Image Recoloring Induced by Palette Color Associations", Journal of WSCG, 2003, Vol. 11, No. 1, pp. 189-196, 2003. DOI:10.1.1.14.3993
  21. Jiaya Jia, Chi-Keung Tang, Yu-Wing Tai, "Local Color Transfer via Probabilistic Segmentation by Expectation-Maximization", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 747-754, 2005. DOI:10.1109/CVPR.2005.215
  22. V. Konushin, V. Vezhnevets, "Interactive Image Colorization and Recoloring Based on Coupled Map Lattices", Graphicon'2006 Conference Proceedings, pp. 231-234, 2006. DOI:10.1.1.178.3475
  23. Yang, Chuan-Kai and Peng, Li-Kai, "Automatic mood transferring between color images", IEEE Computer Graphics and Applications, Vol. 28, No 2, 2008. DOI:10.1109/MCG.2008.24
  24. Cho J, Pappagari R, Kulkarni P, Villalba J, Carmiel Y, Dehak N. "Deep neural networks for emotion recognition combining audio and transcripts". arXiv preprint arXiv:1911.00432. 2019. DOI:10.21437/Interspeech.2018-2466
  25. Dominguez-Jimenez, J.A., Campo-Landines, K.C., Martinez-S antos, J.C., Delahoz, E.J. and Contreras-Ortiz, S.H., "A machine learning model for emotion recognition from physiological signals. Biomedical Signal Processing and Control, Vol. 55, pp.101646, 2020. DOI:10.1016/j.bspc.2019.101646