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Fingerprint image enhancement using multiple filters
Biometrics is the measurement of an individual’s distinctive physical and behavioral characteristics. In comparison to traditional token-based or knowledge-based forms of identification, biometrics such as fingerprints, are more reliable. Fingerprint images recorded digitally can be affected by scan...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280261/ https://www.ncbi.nlm.nih.gov/pubmed/37346560 http://dx.doi.org/10.7717/peerj-cs.1183 |
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author | Shams, Haroon Jan, Tariqullah Khalil, Amjad Ali Ahmad, Naveed Munir, Abid Khalil, Ruhul Amin |
author_facet | Shams, Haroon Jan, Tariqullah Khalil, Amjad Ali Ahmad, Naveed Munir, Abid Khalil, Ruhul Amin |
author_sort | Shams, Haroon |
collection | PubMed |
description | Biometrics is the measurement of an individual’s distinctive physical and behavioral characteristics. In comparison to traditional token-based or knowledge-based forms of identification, biometrics such as fingerprints, are more reliable. Fingerprint images recorded digitally can be affected by scanner noise, incorrect finger pressure, condition of the finger’s skin (wet, dry, or abraded), or physical material it is scanned from. Image enhancement algorithms applied to fingerprint images remove noise elements while retaining relevant structures (ridges, valleys) and help in the detection of fingerprint features (minutiae). Amongst the most common image enhancement filters is the Gabor filter, however, given their restricted maximum bandwidth as well as limited range of spectral information, it falls short. We put forward a novel method of fingerprint image enhancement using a combination of a diffusion-coherence filter and a 2D log-Gabor filter. The log-Gabor overcomes the limitations of the Gabor filter while Coherence Diffusion mitigates noise elements within fingerprint images. Implementation is done on the FVC image database and assessed via visual comparison with coherence diffusion used disjointedly and with the Gabor filter. |
format | Online Article Text |
id | pubmed-10280261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102802612023-06-21 Fingerprint image enhancement using multiple filters Shams, Haroon Jan, Tariqullah Khalil, Amjad Ali Ahmad, Naveed Munir, Abid Khalil, Ruhul Amin PeerJ Comput Sci Bioinformatics Biometrics is the measurement of an individual’s distinctive physical and behavioral characteristics. In comparison to traditional token-based or knowledge-based forms of identification, biometrics such as fingerprints, are more reliable. Fingerprint images recorded digitally can be affected by scanner noise, incorrect finger pressure, condition of the finger’s skin (wet, dry, or abraded), or physical material it is scanned from. Image enhancement algorithms applied to fingerprint images remove noise elements while retaining relevant structures (ridges, valleys) and help in the detection of fingerprint features (minutiae). Amongst the most common image enhancement filters is the Gabor filter, however, given their restricted maximum bandwidth as well as limited range of spectral information, it falls short. We put forward a novel method of fingerprint image enhancement using a combination of a diffusion-coherence filter and a 2D log-Gabor filter. The log-Gabor overcomes the limitations of the Gabor filter while Coherence Diffusion mitigates noise elements within fingerprint images. Implementation is done on the FVC image database and assessed via visual comparison with coherence diffusion used disjointedly and with the Gabor filter. PeerJ Inc. 2023-01-03 /pmc/articles/PMC10280261/ /pubmed/37346560 http://dx.doi.org/10.7717/peerj-cs.1183 Text en ©2023 Shams et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Shams, Haroon Jan, Tariqullah Khalil, Amjad Ali Ahmad, Naveed Munir, Abid Khalil, Ruhul Amin Fingerprint image enhancement using multiple filters |
title | Fingerprint image enhancement using multiple filters |
title_full | Fingerprint image enhancement using multiple filters |
title_fullStr | Fingerprint image enhancement using multiple filters |
title_full_unstemmed | Fingerprint image enhancement using multiple filters |
title_short | Fingerprint image enhancement using multiple filters |
title_sort | fingerprint image enhancement using multiple filters |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280261/ https://www.ncbi.nlm.nih.gov/pubmed/37346560 http://dx.doi.org/10.7717/peerj-cs.1183 |
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