### Abstract

This paper proposes a triplet based fingerprint indexing algorithm which selects the candidates for identification from a large number of enrolled fingerprints. Previous triplet based indexing algorithms have three problems: quantization error, triplet matching error, the proportional increase of similarity score to the number of enrolled triplets. The proposed algorithm solves these problems as follows. First, we generate weighted indices through fuzzy membership functions based on the statistics to reduce quantization error. Second, we apply Geometric Relationships to reduce triplet matching error. Finally, we normalize similarity score to solve the last problem. We compare the proposed algorithm with the previous triplet approach and the Fingercode. Experimental results show that the average rank of the enrolled fingerprint which is identical to an input fingerprint, becomes 2.01 times less than the previous triplet method, and becomes 0.4 times less than the Fingercode.

Original language | English |
---|---|

Pages (from-to) | 584-591 |

Number of pages | 8 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 2688 |

Publication status | Published - 2003 Dec 1 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)

### Cite this

}

**An improved fingerprint indexing algorithm based on the triplet approach.** / Choi, Kyoungtaek; Lee, Dongjae; Lee, Sanghoon; Kim, Jaihie.

Research output: Contribution to journal › Article

TY - JOUR

T1 - An improved fingerprint indexing algorithm based on the triplet approach

AU - Choi, Kyoungtaek

AU - Lee, Dongjae

AU - Lee, Sanghoon

AU - Kim, Jaihie

PY - 2003/12/1

Y1 - 2003/12/1

N2 - This paper proposes a triplet based fingerprint indexing algorithm which selects the candidates for identification from a large number of enrolled fingerprints. Previous triplet based indexing algorithms have three problems: quantization error, triplet matching error, the proportional increase of similarity score to the number of enrolled triplets. The proposed algorithm solves these problems as follows. First, we generate weighted indices through fuzzy membership functions based on the statistics to reduce quantization error. Second, we apply Geometric Relationships to reduce triplet matching error. Finally, we normalize similarity score to solve the last problem. We compare the proposed algorithm with the previous triplet approach and the Fingercode. Experimental results show that the average rank of the enrolled fingerprint which is identical to an input fingerprint, becomes 2.01 times less than the previous triplet method, and becomes 0.4 times less than the Fingercode.

AB - This paper proposes a triplet based fingerprint indexing algorithm which selects the candidates for identification from a large number of enrolled fingerprints. Previous triplet based indexing algorithms have three problems: quantization error, triplet matching error, the proportional increase of similarity score to the number of enrolled triplets. The proposed algorithm solves these problems as follows. First, we generate weighted indices through fuzzy membership functions based on the statistics to reduce quantization error. Second, we apply Geometric Relationships to reduce triplet matching error. Finally, we normalize similarity score to solve the last problem. We compare the proposed algorithm with the previous triplet approach and the Fingercode. Experimental results show that the average rank of the enrolled fingerprint which is identical to an input fingerprint, becomes 2.01 times less than the previous triplet method, and becomes 0.4 times less than the Fingercode.

UR - http://www.scopus.com/inward/record.url?scp=35248818393&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35248818393&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35248818393

VL - 2688

SP - 584

EP - 591

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -