DocumentCode
2655962
Title
Facial expression recognition using RBF neural network based on improved artificial fish swarm algorithm
Author
Ye, Wang ; Xiaojun, Wu ; Shitong, Wang ; Jingyu, Yang
Author_Institution
Sch. of Inf. Technol., Jiangnan Univ., Wuxi
fYear
2008
fDate
16-18 July 2008
Firstpage
416
Lastpage
420
Abstract
Artificial fish swarm algorithm (AFSA) is a global optimization method proposed recently. After analyzing the disadvantages of AFSA, this paper introduced best-step operator and refined the prey behavior. An improved artificial fish-swarm algorithm for the RBF neural network and a model based on this method is developed. Finally the new algorithm is applied to the problem of expression recognition. The research indicates that the new algorithm has some advantages in terms of convergence performance, recognition rate and so on.
Keywords
face recognition; optimisation; radial basis function networks; RBF neural network; artificial fish swarm algorithm; facial expression recognition; global optimization method; Active shape model; Artificial neural networks; Eyebrows; Eyes; Face recognition; Feature extraction; Humans; Marine animals; Morphology; Mouth; Artificial fish-swarm algorithm; Best-step; Facial expression recognition; RBF NN;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location
Kunming
Print_ISBN
978-7-900719-70-6
Electronic_ISBN
978-7-900719-70-6
Type
conf
DOI
10.1109/CHICC.2008.4604925
Filename
4604925
Link To Document