Facial expression recognition based on C-means and K-nearest neighbor algorithms
Hong Wang · Caai Transactions on Intelligent Systems · 2008
With the rapid development of artificial intelligence and pattern recognition, facial expression recognition plays an important role in intelligent human-machine interaction. In this paper, a facial expression classification method is presented which uses a C-means and K-nearest neighbor algorithm as the basis of analysis for the classification of facial expressions. First the images to be analyzed are transformed with Gabor wavelets, and then Fisherface discriminate analysis is perfosmed to generate a feature space. Next, the images which were transformed with Gabor wavelets are projected into the feature space and C-means clustering performed on the projected images to generate sub-expression templates. Finally, the type of expression is identified by comparing the input expression images with the sub-expression templates by using a K-nearest neighbor algorithm. Experiments on the public Japanese female facial expression database show that the method proposed in this paper can achieve a 95.8% recognition rate.