Recognition of fingerprints enhanced by contourlet transform with artificial neural networks

In order to ensure that the performance of a fingerprint recognition system will be powerful with respect to the quality of input fingerprint images, it is essential which requires the enhancement of fingerprint images. Two methods are proposed for fingerprint image enhancement in this study. The first one is performed using local histogram equalization and noise reduction filters. In the second method a wavelet transform and a contourlet transform which is a new extension of the wavelet transform in two dimensions are applied. A method is also developed to recognize fingerprints by using artificial neural network (ANN) which is trained by dataset, called FingerCode, obtained from a filterbank. For every fingerprint, fixed length feature vectors are obtained and these vectors are applied a matching process by using ANN. Experimental results show that the recognition rate of ANN using the obtained feature vectors after enhanced by contourlet transform is 99.6%.