Fingerprint recognition is a very important question in pattern recognition. Combined with wavelet transform (WT)， two-dimensional principal component analysis(2DPCA) and Ellipsoidal Basis Function (EBF)

a novel fingerprint recognition algorithm has been proposed in this paper

which is based on wavelet transform

two-dimensional principal component analysis and EBF neural network. Firstly

original images are decomposed into high-frequency and low-frequency components with the help of WT

and high-frequency components are ignored

so the prime features of original images can be attained．Secondly

the projected features are solved by 2DPCA．Finally，fingerprint recognition can be realized by Ellipsoidal Basis Function Neural Network(EBFNN). The algorithm combines the optimization of the 2DPCA and the adaptability of EBFNN. The experimental results based on FVC2000 have verified that the proposed algorithm has higher recognition rate than WT-PNN and WT-2DPCA-RBF.