DocumentCode :
2775775
Title :
Fingerprint Reconstruction Method Using Partial Differential Equation and Exemplar-Based Inpainting Methods
Author :
Rahmes, Mark ; Allen, Josef DeVaughn ; Elharti, Abdelmoula ; Tenali, Gnana Bhaskar
Author_Institution :
Harris Corp., Melbourne
fYear :
2007
fDate :
11-13 Sept. 2007
Firstpage :
1
Lastpage :
6
Abstract :
Manual latent fingerprint reconstruction to restore missing ridges is a tedious, time consuming, and expensive process. Latent fingerprint ridges are typically partially smudged, partially missing, aged, etc. This type of fingerprint cannot be used in the court of law directly to garner a conviction unless it can be matched to a known fingerprint. However, latent prints minimize the search for potential suspects and finding missing people. We propose an automated reconstruction method which minimizes manual restoration. Our nonlinear partial differential equation (PDE) and exemplar inpainting processes can aid the fingerprint expert. Larger missing regions are repaired using our coherent-based exemplar inpainting algorithm. PDE inpainting is used to fill small fissures in ridge structure. Ridge-lines are sharpened with anisotropic diffusion filters. These technologies improve latent fingerprint computer matching by allowing more minutiae. Accuracy assessment for inpainting missing ridges is described.
Keywords :
Bayes methods; diffusion; filtering theory; fingerprint identification; image matching; image restoration; neural nets; nonlinear equations; partial differential equations; Bayesian-based nodal neural network; PDE inpainting; anisotropic diffusion filters; automated fingerprint reconstruction method; exemplar-based inpainting methods; latent fingerprint computer matching; manual restoration minimization; missing ridge restoration; nonlinear partial differential equation; ridge-line sharpening; Bayesian methods; Biometrics; Communication system software; Filling; Fingerprint recognition; Fingers; Image restoration; Neural networks; Partial differential equations; Reconstruction algorithms; Bayesian-based nodal neural network; Exemplar; Partial Differential Equation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biometrics Symposium, 2007
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-1549-6
Electronic_ISBN :
978-1-4244-1549-6
Type :
conf
DOI :
10.1109/BCC.2007.4430539
Filename :
4430539
Link To Document :
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