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
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;
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
DOI :
10.1109/CHICC.2008.4604925