Facial expression recognition with contextualized histograms

A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted from two widely used descriptors—the local binary pattern (LBP) and weber local descriptor (WLD). The LBP and WLD feature histograms were extracted separately from each facial image, and contextualized histogram was generated as feature vectors to feed the classifier. In addition, the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine (SVM) as classifier, the experimental results on the 2D texture images from the 3D-BUFE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.