Facial Expression Recognition Using 3D Convolutional Neural Network

Abstract: 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.

Young-Hyen Byeon and Keun-Chang Kwak*. “Facial Expression Recognition Using 3D Convolutional Neural Network”. International Journal of Advanced Computer Science and Applications (IJACSA) 5.12 (2014). http://dx.doi.org/10.14569/IJACSA.2014.051215

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