Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Face Expression Based on Deep Learning

딥러닝 기반의 얼굴영상에서 표정 검출에 관한 연구

  • Won, Chulho (Dept. of Bio-Medical Eng., Gyungil University) ;
  • Lee, Bub-ki (Korea Technology Finance Corporation)
  • Received : 2018.07.13
  • Accepted : 2018.07.24
  • Published : 2018.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, researches using LBP and SVM have been performed as one of the image - based methods for facial emotion recognition. LBP, introduced by Ojala et al., is widely used in the field of image recognition due to its high discrimination of objects, robustness to illumination change, and simple operation. In addition, CS(Center-Symmetric)-LBP was used as a modified form of LBP, which is widely used for face recognition. In this paper, we propose a method to detect four facial expressions such as expressionless, happiness, surprise, and anger using deep neural network. The validity of the proposed method is verified using accuracy. Based on the existing LBP feature parameters, it was confirmed that the method using the deep neural network is superior to the method using the Adaboost and SVM classifier.

Keywords

References

  1. T.F. Cootes, G.J. Edwards, and C.J. Taylor, "Active Appearance Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 6, pp. 681-685, 2001. https://doi.org/10.1109/34.927467
  2. Y. Cheon and D. Kim, "A Natural Facial Expression Recognition Using Differential-AAM and k-NNS," Pattren Recognition, Vol. 42, No. 7, pp. 1340-1350, 2008.
  3. A.B. Ashraf, K. Prkachin, T. Chen, S. Lucey, P. Solomon, Z. Ambadar, et al., "The Painful Face-pain Expression Recognition Using Active Appearance Models," Image and Vision Computing, Vol. 12, No. 3, pp. 1788-1796, 2009.
  4. N.D. Matthew, W. Garrison1, P. Curtis, and A. Ralph, "EMPATH: A Neural Network that Categorizes Facial Expressions," Journal of Cognitive Neuroscience, Vol. 14, No. 8, pp. 1158-1173, 2002. https://doi.org/10.1162/089892902760807177
  5. I. Kotsia, I. Buciu, and I. Pitas, "An Analysis of Facial Expression Recognition under Partial Facial Image Occlusion," Image and Vision Computing, Vol. 26, No. 7, pp. 1052-1067, 2008. https://doi.org/10.1016/j.imavis.2007.11.004
  6. T. Ojala, M. Pietikainen, and T. Maenpaa, "Multi-resolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, pp. 971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  7. C. Shan, S. Gong, and P.W. McOwan, "Facial Expression Recognition Based on Local Binary Patterns: A Comprehensive Study," Image and Vision Computing, Vol. 27, Issue 6, pp. 803-816, 2009. https://doi.org/10.1016/j.imavis.2008.08.005
  8. K.T. Lim and C. Won, "Face Image Analysis Using Adaboost Learning and Non-Square Differential LBP," Journal of Korea Multimedia Society, Vol. 19, No. 6, pp. 1014-1023, 2016. https://doi.org/10.9717/kmms.2016.19.6.1014
  9. S. Junding, Z. Shisong, and W. Xiaosheng, "Image Retrieval Based on an Improved CSLBP Descriptor," Proceeding of The 2nd IEEE International Conference on Information Management and Engineering, pp. 115-117, 2010.
  10. Y. Freund and R.E. Schapire, "A Short Introduction to Boosting," Journal of Japanese Society for Artificial Intelligence, Vol. 14, No. 5, pp. 771-780, 1999.
  11. C. Cortes and V. Vapnik, "Support-Vector Networks," Journal of Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995.
  12. H. Kang, K.T. Lim, and C. Won, "Learning Directional LBP Features and Discriminative Feature Regions for Facial Expression Recognition," Journal of Korea Multimedia Society, Vol. 20, No. 5, pp. 748-757, 2017. https://doi.org/10.9717/kmms.2017.20.5.748