Nonlinear Fuzzy Robust PCA on Shape Modelling of Active Appearance Model for Facial Expression Recognition

Automatic facial expression recognition is one of the potential research area in the field of computer vison. It aims to improve the ability of machine to capture social signals in human. Automatic facial expression recognition is still a challenge. We proposed method using contrast limited adaptive histogram equalization (CLAHE) for pre-processing stage then performed feature extraction using active appearance model (AAM) based on nonlinear fuzzy robust principal component analysis (NFRPCA). The feature extraction results will be classified with support vector machine (SVM). Feature points generated AAM based on NFRPCA more adaptive compared to AAM based PCA. Our proposed method's the average accuracy rate reached 96,87% and 93,94% for six and seven basic emotions respectively.