Abstract: A fingerprint identification system from a gray scale image of the fingerprint includes the steps of converting fingerprint image into a digitized gray scale image, enhancing gray scale to provide uniform contrast over entire fingerprint image, down-sampling the contrast enhanced image to limit the number of pixels analyzed, generating a gradient map of the fingerprint image, defining a calculation kernel over which ridge angle determination is made by reference to gradient map, causing the calculation kernel to traverse over gradient map in a predetermined increment over pixels comprising the fingerprint image, smoothing the ridge angle map using the process of Fourier filtering, and smoothing the image quality map using the process of erosion and dilation. The ridge angle map and image quality maps are recorded.

Abstract: In a method for comparing one fingerprint to another, there is recorded for each fingerprint the location and angle of minutiae along with a quality measure of each minutiae, and the core and delta location along with the local average image. An image state map defining high quality image areas with and without minutiae and areas of low image quality is also recorded. In matching fingerprints, ridge angle maps are used to align fingerprint images, then state maps are compared, followed by a comparison of minutiae locations.

Abstract: A method for locating minutia in a gray scale image of a fingerprint using a pair of filters, called "Gabor" and "Minutia" filters, includes determining direction and spacing of ridges at regularly spaced pixels, aligning filters by ridge direction, determining outputs of filters where Gabor Filter has high level output when in parallel ridge flow and, due to phase discontinuity at a minutia, low level output when in neighborhood of a minutia. The Minutia filter exhibits opposite phenomenon, having low level output in parallel ridge flow and high output in neighborhood of a minutia. A method for creating a state map of a fingerprint includes determining image quality, identifying minutia locations, determining areas of good quality where minutia are present, areas of good quality where no minutia are present and areas where quality is below a predetermined value to reliably determine presence or absence of minutia.