Deep Learning in Palmprint Recognition-A Comprehensive Survey Article Swipe
Chengrui Gao
,
Ziyuan Yang
,
Wei Jia
,
Lu Leng
,
Bob Zhang
,
Andrew Beng Jin Teoh
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32388/yz5tfy
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32388/yz5tfy
Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers’ prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition—often grounded in traditional methodologies—there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.
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- Language
- en
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- https://doi.org/10.32388/yz5tfy
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4406511475Canonical identifier for this work in OpenAlex
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https://doi.org/10.32388/yz5tfyDigital Object Identifier
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Deep Learning in Palmprint Recognition-A Comprehensive SurveyWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-17Full publication date if available
- Authors
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Chengrui Gao, Ziyuan Yang, Wei Jia, Lu Leng, Bob Zhang, Andrew Beng Jin TeohList of authors in order
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https://doi.org/10.32388/yz5tfyPublisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.32388/yz5tfyDirect OA link when available
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Artificial intelligence, Deep learning, Computer science, Psychology, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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166Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.key | 102 |
| abstract_inverted_index.the | 116 |
| abstract_inverted_index.(DL) | 36 |
| abstract_inverted_index.Deep | 34 |
| abstract_inverted_index.This | 82 |
| abstract_inverted_index.been | 38 |
| abstract_inverted_index.fall | 21 |
| abstract_inverted_index.stay | 144 |
| abstract_inverted_index.that | 85 |
| abstract_inverted_index.them | 142 |
| abstract_inverted_index.they | 27 |
| abstract_inverted_index.this | 42, 132 |
| abstract_inverted_index.While | 51 |
| abstract_inverted_index.drive | 150 |
| abstract_inverted_index.focus | 54 |
| abstract_inverted_index.often | 20 |
| abstract_inverted_index.paper | 83, 97, 117 |
| abstract_inverted_index.prior | 32 |
| abstract_inverted_index.short | 22 |
| abstract_inverted_index.tasks | 58 |
| abstract_inverted_index.these | 114 |
| abstract_inverted_index.Beyond | 112 |
| abstract_inverted_index.across | 48, 76, 101 |
| abstract_inverted_index.depend | 29 |
| abstract_inverted_index.facets | 78 |
| abstract_inverted_index.future | 126 |
| abstract_inverted_index.recent | 90 |
| abstract_inverted_index.review | 133 |
| abstract_inverted_index.serves | 134 |
| abstract_inverted_index.tasks, | 103 |
| abstract_inverted_index.widely | 9 |
| abstract_inverted_index.within | 59 |
| abstract_inverted_index.abreast | 145 |
| abstract_inverted_index.address | 41 |
| abstract_inverted_index.applied | 10 |
| abstract_inverted_index.bridges | 84 |
| abstract_inverted_index.current | 119 |
| abstract_inverted_index.diverse | 12 |
| abstract_inverted_index.emerged | 3 |
| abstract_inverted_index.feature | 107 |
| abstract_inverted_index.heavily | 28 |
| abstract_inverted_index.methods | 16 |
| abstract_inverted_index.remains | 66 |
| abstract_inverted_index.surveys | 53 |
| abstract_inverted_index.various | 49 |
| abstract_inverted_index.DL-based | 74 |
| abstract_inverted_index.domains. | 50 |
| abstract_inverted_index.enabling | 141 |
| abstract_inverted_index.examines | 99 |
| abstract_inverted_index.existing | 52 |
| abstract_inverted_index.grounded | 62 |
| abstract_inverted_index.learning | 35 |
| abstract_inverted_index.narrowly | 55 |
| abstract_inverted_index.progress | 100 |
| abstract_inverted_index.research | 72 |
| abstract_inverted_index.resource | 138 |
| abstract_inverted_index.specific | 57 |
| abstract_inverted_index.uncovers | 122 |
| abstract_inverted_index.valuable | 137 |
| abstract_inverted_index.Palmprint | 0 |
| abstract_inverted_index.biometric | 7 |
| abstract_inverted_index.exploring | 73 |
| abstract_inverted_index.including | 104 |
| abstract_inverted_index.palmprint | 18, 60, 80, 94, 153 |
| abstract_inverted_index.progress, | 131 |
| abstract_inverted_index.prominent | 6 |
| abstract_inverted_index.promising | 123 |
| abstract_inverted_index.research. | 127 |
| abstract_inverted_index.reviewing | 89 |
| abstract_inverted_index.successes | 47 |
| abstract_inverted_index.DL-powered | 93 |
| abstract_inverted_index.approaches | 75 |
| abstract_inverted_index.challenges | 120 |
| abstract_inverted_index.identifies | 118 |
| abstract_inverted_index.innovation | 151 |
| abstract_inverted_index.introduced | 39 |
| abstract_inverted_index.knowledge. | 33 |
| abstract_inverted_index.leveraging | 44 |
| abstract_inverted_index.remarkable | 46 |
| abstract_inverted_index.scenarios. | 13 |
| abstract_inverted_index.thoroughly | 88 |
| abstract_inverted_index.Traditional | 14 |
| abstract_inverted_index.capability, | 25 |
| abstract_inverted_index.challenges. | 111 |
| abstract_inverted_index.extraction, | 108 |
| abstract_inverted_index.handcrafted | 15 |
| abstract_inverted_index.limitation, | 43 |
| abstract_inverted_index.recognition | 1, 19 |
| abstract_inverted_index.significant | 68 |
| abstract_inverted_index.technology, | 8 |
| abstract_inverted_index.traditional | 64 |
| abstract_inverted_index.advancements | 91 |
| abstract_inverted_index.cutting-edge | 147 |
| abstract_inverted_index.highlighting | 113 |
| abstract_inverted_index.recognition. | 81, 95, 154 |
| abstract_inverted_index.researchers, | 140 |
| abstract_inverted_index.technologies | 148 |
| abstract_inverted_index.advancements, | 115 |
| abstract_inverted_index.comprehensive | 71 |
| abstract_inverted_index.consolidating | 129 |
| abstract_inverted_index.opportunities | 124 |
| abstract_inverted_index.segmentation, | 106 |
| abstract_inverted_index.representation | 24 |
| abstract_inverted_index.researchers’ | 31 |
| abstract_inverted_index.systematically | 98 |
| abstract_inverted_index.state-of-the-art | 130 |
| abstract_inverted_index.region-of-interest | 105 |
| abstract_inverted_index.recognition—often | 61 |
| abstract_inverted_index.methodologies—there | 65 |
| abstract_inverted_index.security/privacy-oriented | 110 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.96619526 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |