Advancements in Synthetic Generation of Contactless Palmprint Biometrics Using StyleGAN Models Article Swipe
A M Mahmud Chowdhury
,
Md Jahangir Alam Khondkar
,
Masudul H. Imtiaz
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jcp4030032
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jcp4030032
Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jcp4030032
- OA Status
- gold
- Cited By
- 3
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402447951
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402447951Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jcp4030032Digital Object Identifier
- Title
-
Advancements in Synthetic Generation of Contactless Palmprint Biometrics Using StyleGAN ModelsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-11Full publication date if available
- Authors
-
A M Mahmud Chowdhury, Md Jahangir Alam Khondkar, Masudul H. ImtiazList of authors in order
- Landing page
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https://doi.org/10.3390/jcp4030032Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/jcp4030032Direct OA link when available
- Concepts
-
Biometrics, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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27Number 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.generative | 79 |
| abstract_inverted_index.processing | 12 |
| abstract_inverted_index.restricted | 67 |
| abstract_inverted_index.similarity | 157, 181 |
| abstract_inverted_index.uniqueness | 146 |
| abstract_inverted_index.(GAN)-based | 82 |
| abstract_inverted_index.Researchers | 51 |
| abstract_inverted_index.adversarial | 80 |
| abstract_inverted_index.application | 33, 115 |
| abstract_inverted_index.contactless | 110 |
| abstract_inverted_index.investigate | 97 |
| abstract_inverted_index.performance | 99 |
| abstract_inverted_index.recognition | 39, 94 |
| abstract_inverted_index.significant | 5, 202 |
| abstract_inverted_index.traditional | 8 |
| abstract_inverted_index.availability | 70 |
| abstract_inverted_index.demonstrated | 4, 206 |
| abstract_inverted_index.individual). | 50 |
| abstract_inverted_index.performance. | 203 |
| abstract_inverted_index.demonstrating | 201 |
| abstract_inverted_index.StyleGAN3-based | 165 |
| abstract_inverted_index.(Scale-Invariant | 139 |
| abstract_inverted_index.(palmprint-based) | 93 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.83036476 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |