Facial Expression Recognition Using Shearlet Transform and Kirsch Masking In this paper, a new feature extractor is proposed that is combined of Discrete Shearlet Transform (DST) system and Kirsch Compass Kernel (KCK) with Local Binary Pattern (LBP) for extracting features from images. Discrete Shearlet Transform gives multiresolute images of an image by highlighting the major edges along with detail features. On the other hand, Kirsch Compass Kernel (KCK) provides 8 different directional images with prominent edges which sharpen the edges in each direction. These characteristics of DST and KCK help LBP to extract the significant features of an image. The extracted features by LBP help to classify the images by using Support Vector Machine (SVM).

Facial expression recognition based on Gabor feature and neural network With the development of humancomputer interaction, emotional computing has gradually become a hot issue in computer vision research. Human expressions contain a wealth of information. How to make the computer fully extract facial expression information and understand human emotions is an urgent problem to be solved. The difficulty of facial expression recognition lies in facial expression extraction and facial expression classification. This paper analyzes the local features of facial expression extracted by Gabor wavelet transform and performs the various dimensional reduction on the problem of feature redundancy, which balances the feature dimension and The contribution rate of the feature. After extracting the features, a suitable BP neural network is constructed, and the extracted features are trained as a data set to the neural network to obtain a good facial expression classifier.