Abstract—This paper presents a new method to classify facial expressions from frontal pose images. In our method, first Pseudo Zernike Moment Invariant (PZMI) was 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 extracted by PZMI. 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 98.33%. This result leads to a conclusion that the proposed method can ensure a high accuracy rate of classification.
Index Terms—Facial expression classification, pseudo Zernike moment invariant, RBF neural network.
T. B.Long is with Lac Hong University, Dong Nai, 71000, Viet Nam
(e-mail: tblong@ lhu.edu.vn).
L. H.Thai is with the University of Science, Ho Chi Minh city, 70000, Viet Nam (e-mail:firstname.lastname@example.org).
T. Hanh was with Lac Hong University, Dong Nai, 71000, Viet Nam. (e-mail: email@example.com).
Cite: Tran Binh Long, Le Hoang Thai, and Tran Hanh, "Facial Expression Classification Method Based on Pseudo Zernike Moment and Radial Basis Function Network," International Journal of Machine Learning and Computing vol. 2, no. 4, pp. 402-405, 2012.