Fusing DCT and LBP features for expression recognition
Ru Li · Computer Engineering and Applications Journal · 2013
In order to effectively extract facial expression feature, a novel method by fusing Discrete Cosine Transform(DCT)and Local Binary Pattern(LBP)features is proposed for expression recognition in this research. The primary information of the face image is centralized in a small number of DCT coefficients, which are used as the global feature of the expression. The face is divided regularly into small regions, from which LBP histograms are computed and concatenated into a LBP features. Subsequently, weight fusion operation is done on these results that are gotten and the nearest distance classification is used to distinguish each testing expression sample. The experiments on JAFFE and Cohn-Kanade expression database show the method proposed is more effective to represent facial expression feature than the single LBP or DCT feature.