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 :
بازگشت