Enhanced SEA algorithm and fingerprint classification

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageAmerican English
Pages (from-to)295-302
Number of pages8
JournalInternational Journal of Computer Applications in Technology
Issue number4
StatePublished - Nov 2007


  • fingerprint
  • Fingerprint classification
  • Singular points
  • SPs
  • fault line
  • Directional image

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