Facial expression recognition with dynamic Gabor volume feature

Facial expression recognition is a long standing problem in affective computing community. A key step is extracting effective features from face images. Gabor filters have been widely used for this purpose. However, a big challenge for Gabor filters is its high dimensionality. In this paper, we propose an efficient feature called dynamic Gabor volume feature (DGVF) based on Gabor filters while with a lower dimensionality for facial expression recognition. In our approach, we first apply Gabor filters with multi-scale and multi-orientation to extract different Gabor faces. And these Gabor faces are arranged into a 3-D volume and Histograms of Oriented Gradients from Three Orthogonal Planes (HOG-TOP) are further employed to encode the 3-D volume in a compact way. Finally, SVM is trained to perform the classification. The experiments conducted on the Extended Cohn-Kanade (CK+) Dataset show that the proposed DGVF is robust to capture and represent the facial appearance features. And our method also achieves a superior performance compared with the other state-of-the-art methods.