PCASYS: A Pattern-Level Classification Automation System for Fingerprints

摘要

This report describes a system we have developed that automatically classifiesimages of fingerprints into six pattern-level classes. 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.