Facial Expression Recognition Using 3D Convolutional Neural Network

This paper is concerned with video-based facial expression recognition frequently used in conjunction with HRI (Human-Robot Interaction) that can naturally interact between human and robot. For this purpose, we design a 3D-CNN(3D Convolutional Neural Networks) by augmenting dimensionality reduction methods such as PCA(Principal Component Analysis) and TMPCA(Tensor-based Multilinear Principal Component Analysis) to recognize simultaneously the successive frames with facial expression images obtained through video camera. The 3D-CNN can achieve some degree of shift and deformation invariance using local receptive fields and spatial subsampling through dimensionality reduction of redundant CNN’s output. The experimental results on video-based facial expression database reveal that the presented method shows a good performance in comparison to the conventional methods such as PCA and TMPCA.

@article{Byeon2014,
  title     = {Facial Expression Recognition Using 3D Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {12},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Young-Hyen Byeon and Keun-Chang Kwak*},
  doi       = {10.14569/IJACSA.2014.051215},
  url       = {https://doi.org/10.14569/IJACSA.2014.051215}
}