Document Type
Dissertation
Date of Award
Fall 1-31-1998
Degree Name
Doctor of Philosophy in Computing Sciences - (Ph.D.)
Department
Computer and Information Science
First Advisor
Daochuan Hung
Second Advisor
James A. McHugh
Third Advisor
Peter A. Ng
Fourth Advisor
Frank Y. Shih
Fifth Advisor
Nirwan Ansari
Sixth Advisor
Frederic P. Fischer
Abstract
Fingerprints can be classified into millions of groups by quantitative measurements of their new representations - Fault-Line Analytic Graphs (FLAG), which describe the relationship between ridge flows and singular points. This new model is highly mathematical, therefore, human interpretation can be reduced to a minimum and the time of identification can be significantly reduced.
There are some well known features on fingerprints such as singular points, cores and deltas, which are global features which characterize the fingerprint pattern class, and minutiae which are the local features which characterize an individual fingerprint image. Singular points are more important than minutiae when classifying fingerprints because the geometric relationship among the singular points decide the type of fingerprints.
When the number of fingerprint records becomes large, the current methods need to compare a large number of fingerprint candidates to identify a given fingerprint. This is the result of having a few synthetic types to classify a database with millions of fingerprints. It has been difficult to enlarge the minter of classification groups because there was no computational method to systematically describe the geometric relationship among singular points and ridge flows. In order to define a more efficient classification method, this dissertation also provides a systematic approach to detect singular points with almost pinpoint precision of 2x2 pixels using efficient algorithms.
Recommended Citation
Huang, Ching-Yu, "FLAG : the fault-line analytic graph and fingerprint classification" (1998). Dissertations. 947.
https://digitalcommons.njit.edu/dissertations/947
