Final expression classification based on pseaudo zernike moment invariant, zernike moment invariant, principal component analysis and radial basis function neural network - Long, Trần Bình; Hanh, Trần (Journal of Computer Science and Cybernetics, 2012)
This paper presents a new method to classify facial expressions from frontal pose images. In our method, first Pseudo Zernike Moment Invariant (PZMI), Zernike Moment Invariant (ZMI) and Principal Component Analysis (PCA) were used to extract features from the global information of the images and then Radial Basis Function (RBF) Network was employed to classify the facial expressions, based on the features which had been already extracted by PZMI, ZMI, PCA. Also, the images were preprocessed to enhance their gray-level, which helps to increase the accuracy of classification. For JAFFE facial expression database, the achieved rate of classification in our experiment is 98.76%. This result leads to a conclusion that the proposed method can ensure a high accuracy rate of classification.