Abstract
One of the most important challenges for face recognition algorithms is dealing with large variability due to facial expression. This paper presents an approach for the expression classification of 3D face scans. The proposed method is based on modelling local deformations which are calculated as the surface change between a neutral face and a face with expression. These deformations are used to train a multiclass/multi-feature LDA classifier. On an unseen face local deformations are calculated automatically using a face with neutral expression as a reference. It is shown that the results obtained are comparable with other similar approaches with the advantage that there is not manual intervention is required for the classification process.