MAP-MRF-based pose computation in fingerprint identification

Yuliang He; Jie Tian

doi:10.1117/12.542203

MAP-MRF-based pose computation in fingerprint identification

Yuliang He
, Jie Tian^*

^*Corresponding author for this work

CAS - Institute of Automation
University of Chinese Academy of Sciences

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper introduces a MAP-MRF (Maximum A posteriori Probability estimation of Markov Random Field) model using the relational structures among features for geometric transformation in fingerprint identification. Additionally, relational projected distances, a novel derivative features among a pair of minutiae, is used for transformation-invariant description. The promising experimental results of the method show that it is effective.

Original language	English
Pages (from-to)	486-491
Number of pages	6
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5404
DOIs	https://doi.org/10.1117/12.542203
State	Published - 2004
Externally published	Yes
Event	Biometric Technology for Human Identification - Orlando, FL, United States Duration: 12 Apr 2004 → 13 Apr 2004

Keywords

Fingerprint identification
Geometric transformation
MAP-MRF model
Relational projected distance

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10.1117/12.542203

Cite this

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title = "MAP-MRF-based pose computation in fingerprint identification",

abstract = "This paper introduces a MAP-MRF (Maximum A posteriori Probability estimation of Markov Random Field) model using the relational structures among features for geometric transformation in fingerprint identification. Additionally, relational projected distances, a novel derivative features among a pair of minutiae, is used for transformation-invariant description. The promising experimental results of the method show that it is effective.",

keywords = "Fingerprint identification, Geometric transformation, MAP-MRF model, Relational projected distance",

author = "Yuliang He and Jie Tian",

year = "2004",

doi = "10.1117/12.542203",

language = "英语",

volume = "5404",

pages = "486--491",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

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note = "Biometric Technology for Human Identification ; Conference date: 12-04-2004 Through 13-04-2004",

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AU - He, Yuliang

AU - Tian, Jie

PY - 2004

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AB - This paper introduces a MAP-MRF (Maximum A posteriori Probability estimation of Markov Random Field) model using the relational structures among features for geometric transformation in fingerprint identification. Additionally, relational projected distances, a novel derivative features among a pair of minutiae, is used for transformation-invariant description. The promising experimental results of the method show that it is effective.

KW - Fingerprint identification

KW - Geometric transformation

KW - MAP-MRF model

KW - Relational projected distance

UR - https://www.scopus.com/pages/publications/8844277750

U2 - 10.1117/12.542203

DO - 10.1117/12.542203

M3 - 会议文章

AN - SCOPUS:8844277750

SN - 0277-786X

VL - 5404

SP - 486

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JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Biometric Technology for Human Identification

Y2 - 12 April 2004 through 13 April 2004

ER -

MAP-MRF-based pose computation in fingerprint identification

Abstract

Keywords

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