doi: 10.7763/IJMLC.2012.V2.153
Facial Expression Classification Method Based on Pseudo Zernike Moment and Radial Basis Function Network
Tran Binh Long1
,
Le Hoang Thai2 ,
Tran Hanh1
- 1Lac Hong University, Dong Nai, 71000, Viet Nam.
- 2University of Science, Ho Chi Minh city, 70000, Viet Nam.
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.
Keywords
- Facial expression classification
- pseudo Zernike moment invariant
- RBF neural network
How to 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, vol. 2, no. 4, pp. 402-405, 2012. https://doi.org/10.7763/IJMLC.2012.V2.153
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).