Facial expression classification on web images

In this paper, we present a novel database which, is obtained from the web. It contains 4761 manually labeled images of seven basic expressions performed by a large number of subjects of different gender, age and ethnicity. Furthermore, we develop feature descriptors based on the discrete cosine transform (DCT), local binary patterns (LBP), and Gabor filters, which share a uniform formulation in terms of regions around key points. We explore several strategies to find an optimal selection of these key points. The system achieves 86.2%, 85.9% and 84.4% accuracy on the web image database using the Gabor, LBP, and DCT descriptors, respectively.