Enhanced SEA algorithm and fingerprint classification

Li-Min Liu, Ching-Yu Huang , Tian-Shyr Dai, George Chang

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文American English
頁(從 - 到)295-302
頁數8
期刊International Journal of Computer Applications in Technology
30
發行號4
DOIs
出版狀態Published - 11月 2007

引用此