Adaptive fingerprint enhancement and identification using linear parametric models

Mehrübe Mehrübeoğlu; Lifford McLauchlan

doi:10.1117/12.795799

15 September 2008 Adaptive fingerprint enhancement and identification using linear parametric models

Proceedings Volume 7073, Applications of Digital Image Processing XXXI; 70731L (2008) https://doi.org/10.1117/12.795799
Event: Optical Engineering + Applications, 2008, San Diego, California, United States

Abstract

Historically, due to its uniqueness and immutability, fingerprints have been used as evidence in criminal cases and in security identification as well as authorization verification applications. In this research, adaptive linear DWT models are developed to describe the fingerprint features (DWT coefficients) to be identified. The proposed model can be used to enhance the fingerprint characteristics identified from fingerprint images to improve recognition. This adaptive model identification technique is then applied to degraded or incomplete fingerprint images to demonstrate the efficacy of the technique under non-ideal conditions. The performance of the method is then compared to previously published research by the authors on identification of degraded fingerprints using PCA-and ICA-based features.

Citation Download Citation

Mehrübe Mehrübeoğlu and Lifford McLauchlan "Adaptive fingerprint enhancement and identification using linear parametric models", Proc. SPIE 7073, Applications of Digital Image Processing XXXI, 70731L (15 September 2008); https://doi.org/10.1117/12.795799

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available