Fingerprint image processing using neural networks

A system for minutiae extraction in fingerprint images using back-propagation networks and Gabor filters is described. Fingerprint images are first convolved with complex Gabor filters and the resulting phase and magnitude signals are passed to networks to identify minutia regions. Promising results are obtained with good detection ratio and low false alarm rate. The importance of having good representations of image data to neural networks is illustrated through variations in performance of different trained networks. The usefulness of Gabor filters in textural image processing and neural networks in image feature extraction is demonstrated