Finger vein is one of the most fitting biometric for identifying individuals. In this paper a new method for finger vein recognition is proposed. First the veins are extracted from finger vein images by using entropy based thresholding. In finger vein images the veins are appeared as dark lines. The method extracts veins as well, but the images are noisy, that means in addition to the veins they have some short and long lines. Then radon transformation are applied to segmented images. The Radon transform is not sensitive to the noise in the images due to its integral nature, so in comparison with other methods is more resistant to noise. For extracting dominant features from finger vein images, common spatial patterns (CSP) is applied to the blocks of radon transformation. Finally the data classified by using nearest neighbor (1-NN) and multilayer perceptron (MLP) neural network. The research was performed on the Peking University finger vein dataset. Experimental results show that 1-NN using CSP, with detecting rate 99.6753%, against MLP is most appropriate for finger vein recognition. Manuscript profile