EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 173 (2018) MATEC Web Conf., 173 (2018) 03066 References
Open Access
Issue
MATEC Web Conf.
Volume 173, 2018
2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
Article Number 03066
Number of page(s) 5
Section Digital Signal and Image Processing
DOI https://doi.org/10.1051/matecconf/201817303066
Published online 19 June 2018
  1. Mehrabian A. Communication without words [J]. Psychology Today, 1968, 2(4): 53～56. [Google Scholar]
  2. Lucey P, Cohn J F, Kanade T, et al. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[C]// Computer Vision and Pattern Recognition Workshops. IEEE, 2010:94-101. [Google Scholar]
  3. Lyons M, Akamatsu S, Kamachi M, et al. Coding Facial Expressions with Gabor Wavelets[C]// IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings. IEEE, 1998:200-205. [CrossRef] [Google Scholar]
  4. Yin L, Wei X, Sun Y, et al. A 3D facial expression database for facial behavior research[C]// International Conference on Automatic Face and Gesture Recognition. IEEE, 2006:211-216. [Google Scholar]
  5. Phillips P J, Flynn P J, Scruggs T, et al. Overview of the Face Recognition Grand Challenge[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2005:947-954. [Google Scholar]
  6. Moreno A B, Sánchez A. GavabDB: a 3D face database[J]. 2004. [Google Scholar]
  7. Sun Y, Yin L J. Facial expression recognition based on 3D dynamic range model sequences[M]// Lecture Notes in Computer Science. Heidelberg: Springer, 2008, 5303: 58-71 [CrossRef] [Google Scholar]
  8. Shao Jie, Dong Nan. Face Natural Expression Recognition Based on RGB-D Dynamic Sequence[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(5): 847-854. [Google Scholar]
  9. Huang D, Shan C F, Ardabilian M, et al. Local binary patterns and its application to facial image analysis: a survey[J]. IEEE Transactions on System, Man, and Cybernetics, 2011, 41(6):765-781. [CrossRef] [Google Scholar]
  10. Sikka K, Wu T, Susskind J, et al. Exploring bag of words architectures in the facial expression domain[C]// International Conference on Computer Vision. Springer-Verlag, 2012:250-259. [Google Scholar]
  11. Ahonen T, Hadid A, Pietikäinen M. Face Recognition with Local Binary Patterns[C]// European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2004:469-481. [Google Scholar]
  12. Viola P, Jones M. Rapid Object Detection using a Boosted Cascade of Simple Features[C]// Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. IEEE, 2003:I-511-I-518 vol.1. [Google Scholar]
  13. Ojala T, Pietikäinen M, Mäenpää T. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[C]// European Conference on Computer Vision. Springer-Verlag, 2000:404-420. [Google Scholar]
  14. Ren S, Girshick R, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137. [Google Scholar]
  15. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105. [Google Scholar]
  16. Y. Li, W. Zheng, Lin Nan. Facial Expression Recognition Based on AAM, CNN and LBP Features[J]. Computer Engineering and Design, 2017(12) [Google Scholar]
  17. Wang J, Yin L, Wei X, et al. 3D Facial Expression Recognition Based on Primitive Surface Feature Distribution[C] IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2006:1399-1406. [Google Scholar]
  18. Drira H, Amor B B, Daoudi M, et al. 3D dynamic expression recognition based on a novel Deformation Vector Field and Random Forest[J]. 2012:1104-1107. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.