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Digital Object Identifier (DOI) : 10.14569/IJACSA.2014.051215
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 12, 2014.
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