DocumentCode :
2112589
Title :
Face Recognition Based on FastICA and RBF Neural Networks
Author :
Zou Mu-chun
Author_Institution :
Sch. of Comput. & Math, YiChun Coll., Yichun
Volume :
1
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
588
Lastpage :
592
Abstract :
The face is a complex multidimensional visual model and it is difficult for face recognition to develop a computational model. This paper, a novel approach is presented to face recognition, which combines Fast independent component analysis (FastICA) and Radial Basis Function neural networks. Firstly, in order to reduce the image data, low-frequency subband images are extracted from original face images by 2D wavelet transform. After, FastICA is applied to extract features from the low-frequency subband image which contains most discriminated information of face image. For reducing computational cost, the improved FastICA method is introduced. Then, RBF neural networks classifier is designed. Lastly, this algorithm is tested on the ORL face databases and the experimental results show that the method has good performance in terms of recognition accuracy and the robustness is enhanced greatly.
Keywords :
face recognition; feature extraction; independent component analysis; radial basis function networks; wavelet transforms; 2D wavelet transform; RBF neural network classifier; computational model; face images; face recognition; fast independent component analysis; feature extraction; low-frequency subband image; multidimensional visual model; radial basis function neural network; Face recognition; Fast independent component analysis; FastICA; Feature extraction; RBF; Radial Basis Function; neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering, 2008. ISISE '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-2727-4
Type :
conf
DOI :
10.1109/ISISE.2008.243
Filename :
4732286
Link To Document :
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