3D Facial Expression Recognition Using Multi-channel Deep Learning Framework (original) (raw)

References

  1. S. Berretti, A.D. Bimbo, P. Pala, B.B. Amor, M. Daoudi, A set of selected SIFT features for 3D facial expression recognition, in International Conference on Pattern Recognition (ICPR), pp. 4125–4128 (2010)
  2. T.F. Cootes, G.J. Edwards, C.J. Taylor, Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)
    Article Google Scholar
  3. T.F. Cootes, C.J. Taylor, D.H. Cooper, J. Graham, Active shape models: their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)
    Article Google Scholar
  4. A. Jan, H. Ding, H. Meng, L. Chen, H. Li, Accurate facial parts localization and deep learning for 3D facial expression recognition, in 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 466–472 (2018)
  5. Y. Lei, M. Bennamoun, A. El-Sallam, An efficient 3D face recognition approach based on the fusion of novel local low-level features. Pattern Recognit. 46(1), 24–37 (2013)
    Article Google Scholar
  6. H. Li, H. Ding, D. Huang, Y. Wang, X. Zhao, J.M. Morvan, L. Chen, An efficient multimodal 2D + 3D feature-based approach to automatic facial expression recognition. Comput. Vis. Image Underst. 140, 83–92 (2015)
    Article Google Scholar
  7. H. Li, J. Sun, Z. Xu, L. Chen, Multimodal 2D+3D facial expression recognition with deep fusion convolutional neural network. IEEE Trans. Multimed. 19, 2816–2831 (2017)
    Article Google Scholar
  8. X. Li, Q. Ruan, Y. Ming, 3D facial expression recognition based on basic geometric features, in IEEE 10th International Conference on Signal Processing (ICSP), pp. 1366–1369 (2010)
  9. X. Li, Q. Ruan, Y. Ming, A remarkable standard for estimating the performance of 3D facial expression features. Neurocomputing 82, 99–108 (2012)
    Article Google Scholar
  10. P. Michel, R. El Kaliouby, Real time facial expression recognition in video using support vector machines, in 5th International Conference on Multimodal interfaces (ICMI) (2003)
  11. I. Mpiperis, S. Malassiotis, M.G. Strintzis, Bilinear models for 3-D face and facial expression recognition. IEEE Trans. Inf. Forensics Secur. 3(3), 498–511 (2008)
    Article Google Scholar
  12. D. O’Shaughnessy, Recognition and processing of speech signals using neural networks. Circuits Syst. Signal Process. 1, 2 (2019). https://doi.org/10.1007/s00034-019-01081-6
    Article Google Scholar
  13. A. Savran, N. Alyüz, H. Dibeklioğlu, O. Çeliktutan, B. Gökberk, B. Sankur, L. Akarun, Bosphorus database for 3D face analysis, in European workshop on Biometrics and Identity Management, pp. 47–56 (2008)
    Google Scholar
  14. H. Soyel, H. Demirel, Facial expression recognition using 3D facial feature distances, in Proceedings of the International Conference on Image Analysis and Recognition, pp. 831–838 (2007)
  15. Q.S. Sun, S.G. Zeng, Y. Liu, P.A. Heng, D.S. Xia, A new method of feature fusion and its application in image recognition. Pattern Recognit. 38, 2437–2448 (2005)
    Article Google Scholar
  16. Y. Sun, X. Chen, M. Rosato, L. Yin, Tracking vertex flow and model adaptation for three-dimensional spatiotemporal face analysis. IEEE Trans. Syst. Man Cybernet. Part A Syst. Hum. 40(3), 461–474 (2010)
    Article Google Scholar
  17. M. Taner Eskil, K.S. Benli, Facial expression recognition based on anatomy. Comput. Vis. Image Underst. 119, 1–14 (2014)
    Article Google Scholar
  18. H. Tang, T. Huang, 3D facial expression recognition based on properties of line segments connecting facial feature points, in 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–6 (2008)
  19. Y.V. Venkatesh, A.A. Kassim, O.V. Ramana Murthy, A novel approach to classification of facial expressions from 3D-mesh datasets using modified PCA. Pattern Recognit. Lett. 30(12), 1128–1137 (2009)
    Article Google Scholar
  20. J. Wang, L. Yin, X. Wei, Y. Sun, 3D facial expression recognition based on primitive surface feature distribution. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 1399–1406 (2006)
    Google Scholar
  21. Y. Wang, M. Gupta, S. Zhang, S. Wang, X. Gu, D. Samaras, P. Huang, High resolution tracking of non-rigid motion of densely sampled 3D data using harmonic maps. Int. J. Comput. Vis. 76(3), 283–300 (2008)
    Article Google Scholar
  22. X. Yang, D. Huang, Y. Wang, L. Chen, Automatic 3D facial expression recognition using geometric scattering representation, in 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–6 (2015)
  23. T. Yun, L. Guan, Human emotion recognition using real 3D visual features from Gabor library, in IEEE International Workshop on Multimedia Signal Processing (MMSP), pp. 505–510 (2010)
  24. K. Yurtkan, H. Demirel, Feature selection for improved 3D facial expression recognition. Pattern Recognit. Lett. 38, 26–33 (2014)
    Article Google Scholar
  25. X. Zhao, E. Dellandréa, J. Zou, L. Chen, A unified probabilistic framework for automatic 3D facial expression analysis based on a Bayesian belief inference and statistical feature models. Image Vis. Comput. 31(3), 231–245 (2013)
    Article Google Scholar
  26. Z. Zeng, M. Pantic, G. Roisman, T. Huang, A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2007)
    Article Google Scholar
  27. L. Zhang, D. Tjondronegoro, Facial expression recognition using facial movement features. IEEE Trans. Affect. Comput. 2(4), 219–229 (2011). https://doi.org/10.1109/T-AFFC.2011
    Article Google Scholar
  28. X. Zhou, H. Seibert, C. Busch, W. Funk, A 3D face recognition algorithm using histogram-based features, in Eurographics Workshop on 3D Object Retrieval (2008)

Download references