Collaborative filtering model for enhancing fingerprint image Article Swipe
Weixin Bian
,
Shifei Ding
,
Weikuan Jia
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1049/iet-ipr.2017.0059
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1049/iet-ipr.2017.0059
Fingerprint enhancement plays a very important role in automatic fingerprint identification system. In order to ensure reliable fingerprint identification and improve fingerprint ridge structure, a novel method based on the collaborative filtering model for fingerprint enhancement is proposed. The proposed method consists of two stages. First, the original fingerprint is pre‐enhanced by using Gabor filter and linear contrast stretching. Next, the pre‐enhanced fingerprint is partitioned into patches in spatial domain, and then the patches are enhanced based on spectra diffusion by using the two‐dimensional (2D) angular‐pass filter and the 2D Butterworth band‐pass filter. The proposed method takes full advantage of the ridge information and spectra diffusion with higher quality to recover the lost ridge information. To evaluate proposed method, the databases FVC2004 are employed, and the comparison experiments are carried out using various methods. Comparative experimental results show that the proposed algorithm outperforms the existing state‐of‐the‐art methods on fingerprint enhancement.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/iet-ipr.2017.0059
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2017.0059
- OA Status
- bronze
- Cited By
- 16
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2765114759
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2765114759Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/iet-ipr.2017.0059Digital Object Identifier
- Title
-
Collaborative filtering model for enhancing fingerprint imageWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-10-23Full publication date if available
- Authors
-
Weixin Bian, Shifei Ding, Weikuan JiaList of authors in order
- Landing page
-
https://doi.org/10.1049/iet-ipr.2017.0059Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2017.0059Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2017.0059Direct OA link when available
- Concepts
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Fingerprint (computing), Computer science, Artificial intelligence, Collaborative filtering, Computer vision, Image (mathematics), Pattern recognition (psychology), Recommender system, Information retrievalTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 1, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.contrast | 57 |
| abstract_inverted_index.enhanced | 75 |
| abstract_inverted_index.evaluate | 116 |
| abstract_inverted_index.existing | 144 |
| abstract_inverted_index.methods. | 133 |
| abstract_inverted_index.original | 47 |
| abstract_inverted_index.proposed | 39, 94, 117, 140 |
| abstract_inverted_index.reliable | 16 |
| abstract_inverted_index.advantage | 98 |
| abstract_inverted_index.algorithm | 141 |
| abstract_inverted_index.automatic | 8 |
| abstract_inverted_index.databases | 120 |
| abstract_inverted_index.diffusion | 79, 105 |
| abstract_inverted_index.employed, | 123 |
| abstract_inverted_index.filtering | 31 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.proposed. | 37 |
| abstract_inverted_index.comparison | 126 |
| abstract_inverted_index.structure, | 23 |
| abstract_inverted_index.Butterworth | 90 |
| abstract_inverted_index.Comparative | 134 |
| abstract_inverted_index.Fingerprint | 0 |
| abstract_inverted_index.band‐pass | 91 |
| abstract_inverted_index.enhancement | 1, 35 |
| abstract_inverted_index.experiments | 127 |
| abstract_inverted_index.fingerprint | 9, 17, 21, 34, 48, 62, 148 |
| abstract_inverted_index.information | 102 |
| abstract_inverted_index.outperforms | 142 |
| abstract_inverted_index.partitioned | 64 |
| abstract_inverted_index.stretching. | 58 |
| abstract_inverted_index.enhancement. | 149 |
| abstract_inverted_index.experimental | 135 |
| abstract_inverted_index.information. | 114 |
| abstract_inverted_index.collaborative | 30 |
| abstract_inverted_index.angular‐pass | 85 |
| abstract_inverted_index.identification | 10, 18 |
| abstract_inverted_index.pre‐enhanced | 50, 61 |
| abstract_inverted_index.two‐dimensional | 83 |
| abstract_inverted_index.state‐of‐the‐art | 145 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5082274996 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I25757504 |
| citation_normalized_percentile.value | 0.87292744 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |