Orientation Field Estimation for Latent Fingerprint Enhancement

Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlappin...

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Vydané v:IEEE transactions on pattern analysis and machine intelligence Ročník 35; číslo 4; s. 925 - 940
Hlavní autori: Jianjiang Feng, Jie Zhou, Jain, A. K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Los Alamitos, CA IEEE 01.04.2013
IEEE Computer Society
Predmet:
Algorithms
Applied sciences
Artificial intelligence
Biometric Identification - methods
Computer science; control theory; systems
Databases, Factual
Dermatoglyphics - classification
Dictionaries
dictionary
Estimation
Exact sciences and technology
Feature extraction
fingerprint enhancement
Fingerprint matching
Humans
Image Processing, Computer-Assisted - methods
Information retrieval. Graph
latent fingerprint
Mathematical model
Noise
Noise measurement
orientation field
Pattern recognition. Digital image processing. Computational geometry
Secret
Smoothing methods
Speech and sound recognition and synthesis. Linguistics
spelling correction
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Theoretical computing
Energy method
Dictionaries
Image processing
Ink
orientation field
Language processing
Natural language
Computer security
fingerprint enhancement
latent fingerprint
Belief
Fingerprint
Terrorism
Criminality
Minimization
Knowledge base
Police
Fingerprint matching
Image quality
Experimental result
dictionary
Fingerprint identification
spelling correction
Probabilistic net
Graph method
Compatibility
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Popis
Shrnutí:Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint orientation field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2012.155