PCASYS - a pattern-level classification automation system for fingerprints

Abstract

This report describes a system we have developed that automatically classifies images of fingerprints into six pattern- level classes. Automatic classification is useful in an Automated Fingerprint Identification System (AFIS) because it can be used to partition the database of fingerprint cards and thereby reduce the amount of work that must be performed by the fingerprint matcher. Our program takes gray-level images of fingerprints as input, and for each fingerprint it produces a hypothesized classification as arch, left loop, right loop, scar, tented arch, or whorl, as well as a. number indicating how much confidence should be assigned to its classification decision. The system performs these processing steps: image segmentation; image enhancement; feature extraction; registration; application of a linear transform that both applies a pattern of regional weights and reduces dimensionality; running of a main classifier, which is a Probabilistic Neural Net, and of an auxiliary whorl-detecting classifier that traces and analyzes pseudoridges (approximate trajectories through the ridge flow); and finally, the combining of the outputs of the main and auxiliary classifiers so as to decide on a hypothesized class and a confidence level. The program's memory and disk space requirements can be met by a typical desktop workstation. The distribution consists of source code, data files, a demonstration set of 2700 fingerprint images, and documentation.