Expression Recognition Using Sparse Selection of log-Gabor Facial Features

Automated expression recognition is a contemporary research field estimating human expressions from image or video data using computer algorithms combined with machine learning. This work proposes an algorithm for expression recognition including a feature extraction algorithm, consisting of log-Gabor filters followed by a feature selection based on sparse approximation of graph embedding. The classification is done on the selected features and is implemented using the Support Vector Machines classifier with radial basis kernel function. The algorithm is tested on the posed facial expressions image database Cohn-Kanade and provides competitive results compared to the state of the art.