Title :
Palmprint recognition based on modified DCT features and RBF neural network
Author :
Yu, Peng-fei ; Xu, Dan
Author_Institution :
Sch. of Inf., Yunnan Univ., Kunming
Abstract :
In this paper, a novel palmprint recognition approach is presented. A modified discrete cosine transform based feature extraction method is used to obtain palmprint features. Furthermore, a radial basis function neural network is employed for palmprint classification. In order to facilitate the training of radial basis function neural network, principal components analysis is applied to reduce these features to a reasonable dimension. The experiment results show that the method is effective.
Keywords :
biometrics (access control); discrete cosine transforms; image recognition; principal component analysis; radial basis function networks; RBF neural network; discrete cosine transform; feature extraction method; palmprint classification; palmprint recognition; principal components analysis; radial basis function neural network; Biometrics; Cybernetics; Data mining; Discrete cosine transforms; Feature extraction; Fingerprint recognition; Machine learning; Neural networks; Principal component analysis; Radial basis function networks; Biometrics; DCT-mod2; PCA; Palmprint recognition; RBF neural network;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620918
