Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach Article Swipe
Banafsheh Adami
,
MohammadReza Hosseinzadehketilateh
,
Nima Karimian
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/ijcb62174.2024.10744434
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/ijcb62174.2024.10744434
This paper presents a novel unsupervised deep learning approach for contactless fingerprint biometric anti-spoofing. Traditional presentation attack detection (PAD) methods rely on supervised learning techniques, utilizing both bonafide and spoofed samples during training. However, these approaches often struggle to generalize well against unseen attacks. In contrast, our proposed method employs an unsupervised autoencoder combined with a convolutional block attention module (CBAM) and is exclusively trained on bonafide images without exposure to spoofed samples. The model is then evaluated against various unseen spoofed samples during the testing phase.The proposed deep learning architecture achieves robust spoof detection by learning meaningful representations of live fingerprints. The convolutional autoencoder captures spatial features, while the attention mechanisms (channel and spatial) enhance the model's focus on relevant regions and dependencies within the input images. This unsupervised approach eliminates the need for labeled spoofed samples during training, making it more scalable and adaptable to real-world scenarios.Extensive experiments were conducted using three public databases: CLARKSON, COLFISPOOF, and IIITD Spoofed Fingerphoto Database. The proposed method demonstrated impressive performance, achieving an average Bonafide Presentation Classification Error Rate (BPCER) of 0.96% and an Attack Presentation Classification Error Rate (APCER) of 1.6% against various types of spoofed samples. These results highlight the effectiveness of our unsupervised deep learning approach in detecting presentation attacks for contactless fingerprint biometric systems.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ijcb62174.2024.10744434
- OA Status
- green
- Cited By
- 4
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404239170
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404239170Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/ijcb62174.2024.10744434Digital Object Identifier
- Title
-
Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning ApproachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-15Full publication date if available
- Authors
-
Banafsheh Adami, MohammadReza Hosseinzadehketilateh, Nima KarimianList of authors in order
- Landing page
-
https://doi.org/10.1109/ijcb62174.2024.10744434Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://figshare.com/articles/conference_contribution/Contactless_Fingerprint_Biometric_Anti-Spoofing_An_Unsupervised_Deep_Learning_Approach/25537381Direct OA link when available
- Concepts
-
Biometrics, Spoofing attack, Computer science, Fingerprint (computing), Artificial intelligence, Fingerprint recognition, Unsupervised learning, Pattern recognition (psychology), Deep learning, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4404239170 |
|---|---|
| doi | https://doi.org/10.1109/ijcb62174.2024.10744434 |
| ids.doi | https://doi.org/10.1109/ijcb62174.2024.10744434 |
| ids.openalex | https://openalex.org/W4404239170 |
| fwci | 2.85001706 |
| type | article |
| title | Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 10 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T10828 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9987000226974487 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1711 |
| topics[0].subfield.display_name | Signal Processing |
| topics[0].display_name | Biometric Identification and Security |
| topics[1].id | https://openalex.org/T11800 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9722999930381775 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1710 |
| topics[1].subfield.display_name | Information Systems |
| topics[1].display_name | User Authentication and Security Systems |
| funders[0].id | https://openalex.org/F4320306076 |
| funders[0].ror | https://ror.org/021nxhr62 |
| funders[0].display_name | National Science Foundation |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C184297639 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8700687885284424 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q177765 |
| concepts[0].display_name | Biometrics |
| concepts[1].id | https://openalex.org/C167900197 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8093794584274292 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11081100 |
| concepts[1].display_name | Spoofing attack |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7183370590209961 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2777826928 |
| concepts[3].level | 2 |
| concepts[3].score | 0.713105320930481 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3745713 |
| concepts[3].display_name | Fingerprint (computing) |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5984011292457581 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C168406668 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5494776964187622 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q178022 |
| concepts[5].display_name | Fingerprint recognition |
| concepts[6].id | https://openalex.org/C8038995 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4611819386482239 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1152135 |
| concepts[6].display_name | Unsupervised learning |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4298159182071686 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C108583219 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4271320700645447 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[8].display_name | Deep learning |
| concepts[9].id | https://openalex.org/C38652104 |
| concepts[9].level | 1 |
| concepts[9].score | 0.25999659299850464 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[9].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/biometrics |
| keywords[0].score | 0.8700687885284424 |
| keywords[0].display_name | Biometrics |
| keywords[1].id | https://openalex.org/keywords/spoofing-attack |
| keywords[1].score | 0.8093794584274292 |
| keywords[1].display_name | Spoofing attack |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7183370590209961 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/fingerprint |
| keywords[3].score | 0.713105320930481 |
| keywords[3].display_name | Fingerprint (computing) |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5984011292457581 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/fingerprint-recognition |
| keywords[5].score | 0.5494776964187622 |
| keywords[5].display_name | Fingerprint recognition |
| keywords[6].id | https://openalex.org/keywords/unsupervised-learning |
| keywords[6].score | 0.4611819386482239 |
| keywords[6].display_name | Unsupervised learning |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.4298159182071686 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/deep-learning |
| keywords[8].score | 0.4271320700645447 |
| keywords[8].display_name | Deep learning |
| keywords[9].id | https://openalex.org/keywords/computer-security |
| keywords[9].score | 0.25999659299850464 |
| keywords[9].display_name | Computer security |
| language | en |
| locations[0].id | doi:10.1109/ijcb62174.2024.10744434 |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | 2024 IEEE International Joint Conference on Biometrics (IJCB) |
| locations[0].landing_page_url | https://doi.org/10.1109/ijcb62174.2024.10744434 |
| locations[1].id | pmh:oai:figshare.com:article/25537381 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400572 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[1].source.host_organization | https://openalex.org/I196829312 |
| locations[1].source.host_organization_name | La Trobe University |
| locations[1].source.host_organization_lineage | https://openalex.org/I196829312 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://figshare.com/articles/conference_contribution/Contactless_Fingerprint_Biometric_Anti-Spoofing_An_Unsupervised_Deep_Learning_Approach/25537381 |
| locations[2].id | doi:10.6084/m9.figshare.25537381.v1 |
| locations[2].is_oa | True |
| locations[2].source | |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.6084/m9.figshare.25537381.v1 |
| indexed_in | crossref, datacite |
| authorships[0].author.id | https://openalex.org/A5092998416 |
| authorships[0].author.orcid | https://orcid.org/0009-0004-0193-577X |
| authorships[0].author.display_name | Banafsheh Adami |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I12097938 |
| authorships[0].affiliations[0].raw_affiliation_string | West Virginia University,Computer Science and Electrical Engineering |
| authorships[0].institutions[0].id | https://openalex.org/I12097938 |
| authorships[0].institutions[0].ror | https://ror.org/011vxgd24 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I12097938 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | West Virginia University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Banafsheh Adami |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | West Virginia University,Computer Science and Electrical Engineering |
| authorships[1].author.id | https://openalex.org/A5094146029 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | MohammadReza Hosseinzadehketilateh |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I12097938 |
| authorships[1].affiliations[0].raw_affiliation_string | West Virginia University,Computer Science and Electrical Engineering |
| authorships[1].institutions[0].id | https://openalex.org/I12097938 |
| authorships[1].institutions[0].ror | https://ror.org/011vxgd24 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I12097938 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | West Virginia University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | MohammadReza Hosseinzadehketilateh |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | West Virginia University,Computer Science and Electrical Engineering |
| authorships[2].author.id | https://openalex.org/A5014914538 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4590-7170 |
| authorships[2].author.display_name | Nima Karimian |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I12097938 |
| authorships[2].affiliations[0].raw_affiliation_string | West Virginia University,Computer Science and Electrical Engineering |
| authorships[2].institutions[0].id | https://openalex.org/I12097938 |
| authorships[2].institutions[0].ror | https://ror.org/011vxgd24 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I12097938 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | West Virginia University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Nima Karimian |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | West Virginia University,Computer Science and Electrical Engineering |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://figshare.com/articles/conference_contribution/Contactless_Fingerprint_Biometric_Anti-Spoofing_An_Unsupervised_Deep_Learning_Approach/25537381 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10828 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9987000226974487 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1711 |
| primary_topic.subfield.display_name | Signal Processing |
| primary_topic.display_name | Biometric Identification and Security |
| related_works | https://openalex.org/W4233349824, https://openalex.org/W3014822659, https://openalex.org/W2117826006, https://openalex.org/W1621827506, https://openalex.org/W4362496757, https://openalex.org/W2051501574, https://openalex.org/W2124627279, https://openalex.org/W2050967184, https://openalex.org/W2566091814, https://openalex.org/W4200422973 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:figshare.com:article/25537381 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400572 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| best_oa_location.source.host_organization | https://openalex.org/I196829312 |
| best_oa_location.source.host_organization_name | La Trobe University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I196829312 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://figshare.com/articles/conference_contribution/Contactless_Fingerprint_Biometric_Anti-Spoofing_An_Unsupervised_Deep_Learning_Approach/25537381 |
| primary_location.id | doi:10.1109/ijcb62174.2024.10744434 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 2024 IEEE International Joint Conference on Biometrics (IJCB) |
| primary_location.landing_page_url | https://doi.org/10.1109/ijcb62174.2024.10744434 |
| publication_date | 2024-09-15 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2265356345, https://openalex.org/W3123137331, https://openalex.org/W1976948919, https://openalex.org/W3202299883, https://openalex.org/W4285117300, https://openalex.org/W2876459260, https://openalex.org/W4205428640, https://openalex.org/W2782304390, https://openalex.org/W1970405634, https://openalex.org/W4313547860, https://openalex.org/W4319336184, https://openalex.org/W2787152054, https://openalex.org/W2917641056, https://openalex.org/W2563686859, https://openalex.org/W2943861836, https://openalex.org/W2794300737, https://openalex.org/W4206338407, https://openalex.org/W4286681712, https://openalex.org/W4382053244, https://openalex.org/W4392411875, https://openalex.org/W4392411982, https://openalex.org/W4308694742, https://openalex.org/W3160766702, https://openalex.org/W4226021186, https://openalex.org/W3212386989, https://openalex.org/W3210720059, https://openalex.org/W4289752563, https://openalex.org/W2550553598, https://openalex.org/W2210638116, https://openalex.org/W4248083651, https://openalex.org/W4383469298, https://openalex.org/W2561238782 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 3, 55 |
| abstract_inverted_index.In | 44 |
| abstract_inverted_index.an | 50, 170, 181 |
| abstract_inverted_index.by | 95 |
| abstract_inverted_index.in | 207 |
| abstract_inverted_index.is | 62, 75 |
| abstract_inverted_index.it | 141 |
| abstract_inverted_index.of | 99, 178, 188, 193, 201 |
| abstract_inverted_index.on | 21, 65, 119 |
| abstract_inverted_index.to | 38, 70, 146 |
| abstract_inverted_index.The | 73, 102, 163 |
| abstract_inverted_index.and | 28, 61, 113, 122, 144, 158, 180 |
| abstract_inverted_index.for | 9, 134, 211 |
| abstract_inverted_index.our | 46, 202 |
| abstract_inverted_index.the | 84, 109, 116, 125, 132, 199 |
| abstract_inverted_index.1.6% | 189 |
| abstract_inverted_index.Rate | 176, 186 |
| abstract_inverted_index.This | 0, 128 |
| abstract_inverted_index.both | 26 |
| abstract_inverted_index.deep | 6, 88, 204 |
| abstract_inverted_index.live | 100 |
| abstract_inverted_index.more | 142 |
| abstract_inverted_index.need | 133 |
| abstract_inverted_index.rely | 20 |
| abstract_inverted_index.then | 76 |
| abstract_inverted_index.well | 40 |
| abstract_inverted_index.were | 150 |
| abstract_inverted_index.with | 54 |
| abstract_inverted_index.(PAD) | 18 |
| abstract_inverted_index.0.96% | 179 |
| abstract_inverted_index.Error | 175, 185 |
| abstract_inverted_index.IIITD | 159 |
| abstract_inverted_index.These | 196 |
| abstract_inverted_index.block | 57 |
| abstract_inverted_index.focus | 118 |
| abstract_inverted_index.input | 126 |
| abstract_inverted_index.model | 74 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.often | 36 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.spoof | 93 |
| abstract_inverted_index.these | 34 |
| abstract_inverted_index.three | 153 |
| abstract_inverted_index.types | 192 |
| abstract_inverted_index.using | 152 |
| abstract_inverted_index.while | 108 |
| abstract_inverted_index.(CBAM) | 60 |
| abstract_inverted_index.Attack | 182 |
| abstract_inverted_index.attack | 16 |
| abstract_inverted_index.during | 31, 83, 138 |
| abstract_inverted_index.images | 67 |
| abstract_inverted_index.making | 140 |
| abstract_inverted_index.method | 48, 165 |
| abstract_inverted_index.module | 59 |
| abstract_inverted_index.public | 154 |
| abstract_inverted_index.robust | 92 |
| abstract_inverted_index.unseen | 42, 80 |
| abstract_inverted_index.within | 124 |
| abstract_inverted_index.(APCER) | 187 |
| abstract_inverted_index.(BPCER) | 177 |
| abstract_inverted_index.Spoofed | 160 |
| abstract_inverted_index.against | 41, 78, 190 |
| abstract_inverted_index.attacks | 210 |
| abstract_inverted_index.average | 171 |
| abstract_inverted_index.employs | 49 |
| abstract_inverted_index.enhance | 115 |
| abstract_inverted_index.images. | 127 |
| abstract_inverted_index.labeled | 135 |
| abstract_inverted_index.methods | 19 |
| abstract_inverted_index.model's | 117 |
| abstract_inverted_index.regions | 121 |
| abstract_inverted_index.results | 197 |
| abstract_inverted_index.samples | 30, 82, 137 |
| abstract_inverted_index.spatial | 106 |
| abstract_inverted_index.spoofed | 29, 71, 81, 136, 194 |
| abstract_inverted_index.testing | 85 |
| abstract_inverted_index.trained | 64 |
| abstract_inverted_index.various | 79, 191 |
| abstract_inverted_index.without | 68 |
| abstract_inverted_index.(channel | 112 |
| abstract_inverted_index.Bonafide | 172 |
| abstract_inverted_index.However, | 33 |
| abstract_inverted_index.achieves | 91 |
| abstract_inverted_index.approach | 8, 130, 206 |
| abstract_inverted_index.attacks. | 43 |
| abstract_inverted_index.bonafide | 27, 66 |
| abstract_inverted_index.captures | 105 |
| abstract_inverted_index.combined | 53 |
| abstract_inverted_index.exposure | 69 |
| abstract_inverted_index.learning | 7, 23, 89, 96, 205 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 47, 87, 164 |
| abstract_inverted_index.relevant | 120 |
| abstract_inverted_index.samples. | 72, 195 |
| abstract_inverted_index.scalable | 143 |
| abstract_inverted_index.spatial) | 114 |
| abstract_inverted_index.struggle | 37 |
| abstract_inverted_index.systems. | 215 |
| abstract_inverted_index.CLARKSON, | 156 |
| abstract_inverted_index.Database. | 162 |
| abstract_inverted_index.achieving | 169 |
| abstract_inverted_index.adaptable | 145 |
| abstract_inverted_index.attention | 58, 110 |
| abstract_inverted_index.biometric | 12, 214 |
| abstract_inverted_index.conducted | 151 |
| abstract_inverted_index.contrast, | 45 |
| abstract_inverted_index.detecting | 208 |
| abstract_inverted_index.detection | 17, 94 |
| abstract_inverted_index.evaluated | 77 |
| abstract_inverted_index.features, | 107 |
| abstract_inverted_index.highlight | 198 |
| abstract_inverted_index.phase.The | 86 |
| abstract_inverted_index.training, | 139 |
| abstract_inverted_index.training. | 32 |
| abstract_inverted_index.utilizing | 25 |
| abstract_inverted_index.approaches | 35 |
| abstract_inverted_index.databases: | 155 |
| abstract_inverted_index.eliminates | 131 |
| abstract_inverted_index.generalize | 39 |
| abstract_inverted_index.impressive | 167 |
| abstract_inverted_index.meaningful | 97 |
| abstract_inverted_index.mechanisms | 111 |
| abstract_inverted_index.real-world | 147 |
| abstract_inverted_index.supervised | 22 |
| abstract_inverted_index.COLFISPOOF, | 157 |
| abstract_inverted_index.Fingerphoto | 161 |
| abstract_inverted_index.Traditional | 14 |
| abstract_inverted_index.autoencoder | 52, 104 |
| abstract_inverted_index.contactless | 10, 212 |
| abstract_inverted_index.exclusively | 63 |
| abstract_inverted_index.experiments | 149 |
| abstract_inverted_index.fingerprint | 11, 213 |
| abstract_inverted_index.techniques, | 24 |
| abstract_inverted_index.Presentation | 173, 183 |
| abstract_inverted_index.architecture | 90 |
| abstract_inverted_index.demonstrated | 166 |
| abstract_inverted_index.dependencies | 123 |
| abstract_inverted_index.performance, | 168 |
| abstract_inverted_index.presentation | 15, 209 |
| abstract_inverted_index.unsupervised | 5, 51, 129, 203 |
| abstract_inverted_index.convolutional | 56, 103 |
| abstract_inverted_index.effectiveness | 200 |
| abstract_inverted_index.fingerprints. | 101 |
| abstract_inverted_index.Classification | 174, 184 |
| abstract_inverted_index.anti-spoofing. | 13 |
| abstract_inverted_index.representations | 98 |
| abstract_inverted_index.scenarios.Extensive | 148 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.88018664 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |