Research On 2-Dimensional Multi-scale Block Local Gabor Binary Patterns Based Expression Recognition And Illumination Estimation

Nowadays, expression recognition has become an active topic in pattern recognition and machine vision community. More and more researchers have focused on this domain. In many applications it is necessary to recognize expression in a single static image. However, due to the fact that less expression information is available in static images, expression recognition from static images is more difficult than that from image sequences. Inorder to achieve better person-independent expression recognition accuracy in static images, two kinds of feature and dichotomy-dependent weights are presented in this paper. And to eliminate the negative effect caused by variant illumination in expression recognition, a 3D representative face and clustering based framework is presented in this paper. The main work and innovations of this paper are as follows:(1)In feature selection, we combine the concept of Multi-scale Gabor analysis with MB-LBP encoding to achieve the so-called MB-LGBP features which is both locally and globally informative.(2)The discrimination of different expressions needs more precise description of local textures. As 1st-order description, LBP can not encode spatial structure information. And this problem can not be solved in nature only by partitioning. So we utilize Gray Level Co-occurrence Matrix instead of the traditional statistical histogram and present the so-called 2-dimensional LBP features. By following the fashion of feature fusion, finally we get the 2D MB-LGBP composite features for classification.(3)In classification, dichotomy-dependent weights for SVM is introduced and its performance is compared with the traditional k-nearest neighbor paradigm based on weighted Chi Square distance. The result we get is promising, which proves the superiority of the 2-dimensional MB-LGBP features to some other popular features in expression recognition.(4)To eliminate the negative effect caused by variant illumination in expression recognition. A 3D representative face (RF) and clustering based framework is presented in this paper, which can estimate 13 illumination conditions under certain poses accurately. By adaptively clustering 3D faces into a number of facial types, subjects with similar facial appearance are clustered together. Then the RF of each cluster is generated, which provides our system the generalization ability to do subject-dependent illumination estimation. Compared with other works which rely on 3D reconstruction, our method has less computation complexity.