Federated Learning Applications in Fingerprint and Finger Vein Recognition
Fingerprints and finger veins are widely used in security identification in many fields due to their uniqueness and identifiability. However, their privacy issues are often criticized. This article summarizes several approaches that combine federated learning with fingerprint and finger vein recogni...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01023.pdf |
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| _version_ | 1825206543451160576 |
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| author | Wang Yongchao |
| author_facet | Wang Yongchao |
| author_sort | Wang Yongchao |
| collection | DOAJ |
| description | Fingerprints and finger veins are widely used in security identification in many fields due to their uniqueness and identifiability. However, their privacy issues are often criticized. This article summarizes several approaches that combine federated learning with fingerprint and finger vein recognition to solve privacy issues. One of the frameworks for fingerprint recognition, Federated Learning-Fingerprint Recognition, uses sparse representation techniques such as the Discrete Cosine Transform for data preprocessing. The framework also references the ResNet18 model and reservoir sampling so that each client can participate in training fairly. As for finger vein recognition, the Federated Learning-based Finger Vein authentication framework allows clients to share model weights to solve the data island problem and divide client data into shared and personalized parts to ensure privacy. This paper also points out its challenges, such as poor interpretability and applicability, and provides optimization solutions. For example, the interpretability issue can be solved by implementing an expert system. The expert system uses its robust knowledge base and inference engine to track model behavior and derive reasonable explanations. Transfer learning can also eliminate the applicability issue. It transfers the knowledge gained from training clients with concentrated data to clients with sparse data. In summary, this article comprehensively reviews the methods of federated learning in fingerprint and finger vein, respectively, and discusses the shortcomings and prospects. |
| format | Article |
| id | doaj-art-10c723aaf11e4392b8645d2a414bc3c7 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-10c723aaf11e4392b8645d2a414bc3c72025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700102310.1051/itmconf/20257001023itmconf_dai2024_01023Federated Learning Applications in Fingerprint and Finger Vein RecognitionWang Yongchao0Computer Science, University of CaliforniaFingerprints and finger veins are widely used in security identification in many fields due to their uniqueness and identifiability. However, their privacy issues are often criticized. This article summarizes several approaches that combine federated learning with fingerprint and finger vein recognition to solve privacy issues. One of the frameworks for fingerprint recognition, Federated Learning-Fingerprint Recognition, uses sparse representation techniques such as the Discrete Cosine Transform for data preprocessing. The framework also references the ResNet18 model and reservoir sampling so that each client can participate in training fairly. As for finger vein recognition, the Federated Learning-based Finger Vein authentication framework allows clients to share model weights to solve the data island problem and divide client data into shared and personalized parts to ensure privacy. This paper also points out its challenges, such as poor interpretability and applicability, and provides optimization solutions. For example, the interpretability issue can be solved by implementing an expert system. The expert system uses its robust knowledge base and inference engine to track model behavior and derive reasonable explanations. Transfer learning can also eliminate the applicability issue. It transfers the knowledge gained from training clients with concentrated data to clients with sparse data. In summary, this article comprehensively reviews the methods of federated learning in fingerprint and finger vein, respectively, and discusses the shortcomings and prospects.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01023.pdf |
| spellingShingle | Wang Yongchao Federated Learning Applications in Fingerprint and Finger Vein Recognition ITM Web of Conferences |
| title | Federated Learning Applications in Fingerprint and Finger Vein Recognition |
| title_full | Federated Learning Applications in Fingerprint and Finger Vein Recognition |
| title_fullStr | Federated Learning Applications in Fingerprint and Finger Vein Recognition |
| title_full_unstemmed | Federated Learning Applications in Fingerprint and Finger Vein Recognition |
| title_short | Federated Learning Applications in Fingerprint and Finger Vein Recognition |
| title_sort | federated learning applications in fingerprint and finger vein recognition |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01023.pdf |
| work_keys_str_mv | AT wangyongchao federatedlearningapplicationsinfingerprintandfingerveinrecognition |