Facial Expression Recognition Based on the Texture Features of Global Principal Component and Local Boundary
Weihao Zheng, Wei Wang, Yi De Ma · Applied Mechanics and Materials · 2014
Facial expression recognition is a key ingredient to either emotion analysis or pattern recognition, which is also an important component in human-machine interaction. In facial expression analysis, one of the well-known methods to obtain the texture of expressions is local binary patterns (LBP) which compares pixels in local region and encodes the comparison result in forms of histogram. However, we argue that the textures of expressions are not accurate and still contain some irrelevant information, especially in the region between eyes and mouth. In this paper, we propose a compound method to recognize expressions by applying local binary patterns to global and local images processed by bidirectional principal component analysis (BDPCA) reconstruction and morphologic preprocess, respectively. It proves that our method can be applied for recognizing expressions by using texture features of global principal component and local boundary and achieves a considerable high accuracy.