Title

Enhanced SEA algorithm and fingerprint classification

Authors

Li Min Liu, Chung Yuan Christian University
Ching Yu Huang, Rutgers School of Dental Medicine
Tian Shyr Dai, National Chiao Tung University
George Chang, Kean University

Document Type

Article

Publication Date

1-1-2007

Abstract

This paper proposes the Enhanced Shrinking and Expanding Algorithm (ESEA) with a new categorisation method. The ESEA overcomes anomalies in the original Shrinking and Expanding Algorithm (SEA) which fails to locate Singular Points (SPs) in many cases. Experimental results show that the accuracy rate of the ESEA reaches 94.7%, a 32.5% increase from the SEA. In the proposed fingerprint categorisation method, each fingerprint will be assigned to a specific subclass. The search for a specific fingerprint can therefore be performed only on specific subclasses containing a small portion of a large fingerprint database, which will save enormous computational time. Copyright © 2007 Inderscience Enterprises Ltd.

Publication Title

International Journal of Computer Applications in Technology

First Page Number

295

Last Page Number

302

DOI

10.1504/IJCAT.2007.017241

Recommended Citation

Liu, Li Min; Huang, Ching Yu; Dai, Tian Shyr; and Chang, George, "Enhanced SEA algorithm and fingerprint classification" (2007). Kean Publications. 2544.
https://digitalcommons.kean.edu/keanpublications/2544