article · 2013

3D Facial Expression Recognition on Curvature Local Binary Patterns

Cited by 6Abstract and details only

Abstract

This paper proposed a new method Based on curvature Based LBP feature to recognize 3D facial expression automatically. 3D facial expression images are described by means of four images which gray level are the value of curvature-Based descriptors (principal curvatures k1, k2, mean curvature, shape index) and then encoded by LBP. To efficiently optimize the performance, Chi-square distance is employed for classification. Finally, experimental result achieved on the Bosphorus database illustrates that the curvature-Based LBP (CLBP) has performs better than other features and also shows these features are significant for 3D facial expression recognition.

Cite this paper

APA 7

Wang, Y., & Meng, M. (2013). 3D Facial Expression Recognition on Curvature Local Binary Patterns. https://doi.org/10.1109/ihmsc.2013.176

MLA 9

Wang, Yiding, and Meng Meng. “3D Facial Expression Recognition on Curvature Local Binary Patterns.” vol. 14, 2013, pp. 123–26, https://doi.org/10.1109/ihmsc.2013.176.

Chicago

Wang, Yiding, and Meng Meng. 2013. “3D Facial Expression Recognition on Curvature Local Binary Patterns.” https://doi.org/10.1109/ihmsc.2013.176.

Harvard

Wang, Y. and Meng, M. (2013) ‘3D Facial Expression Recognition on Curvature Local Binary Patterns’. Available at: https://doi.org/10.1109/ihmsc.2013.176.

BibTeX

@article{wang2013facial,
  title = {{3D Facial Expression Recognition on Curvature Local Binary Patterns}},
  author = {Wang, Yiding and Meng, Meng},
  volume = {14},
  pages = {123--126},
  year = {2013},
  doi = {10.1109/ihmsc.2013.176}
}

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