DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913344633438208 |
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| author | Grabovski, Freddie Yasur, Lior Hacmon, Yaniv Nisimov, Lior Nimrod, Stav |
| author_facet | Grabovski, Freddie Yasur, Lior Hacmon, Yaniv Nisimov, Lior Nimrod, Stav |
| contents | This study explores the generation of synthesized fingerprint images using Denoising Diffusion Probabilistic Models (DDPMs). The significant obstacles in collecting real biometric data, such as privacy concerns and the demand for diverse datasets, underscore the imperative for synthetic biometric alternatives that are both realistic and varied. Despite the strides made with Generative Adversarial Networks (GANs) in producing realistic fingerprint images, their limitations prompt us to propose DDPMs as a promising alternative. DDPMs are capable of generating images with increasing clarity and realism while maintaining diversity. Our results reveal that DiffFinger not only competes with authentic training set data in quality but also provides a richer set of biometric data, reflecting true-to-life variability. These findings mark a promising stride in biometric synthesis, showcasing the potential of DDPMs to advance the landscape of fingerprint identification and authentication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models Grabovski, Freddie Yasur, Lior Hacmon, Yaniv Nisimov, Lior Nimrod, Stav Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This study explores the generation of synthesized fingerprint images using Denoising Diffusion Probabilistic Models (DDPMs). The significant obstacles in collecting real biometric data, such as privacy concerns and the demand for diverse datasets, underscore the imperative for synthetic biometric alternatives that are both realistic and varied. Despite the strides made with Generative Adversarial Networks (GANs) in producing realistic fingerprint images, their limitations prompt us to propose DDPMs as a promising alternative. DDPMs are capable of generating images with increasing clarity and realism while maintaining diversity. Our results reveal that DiffFinger not only competes with authentic training set data in quality but also provides a richer set of biometric data, reflecting true-to-life variability. These findings mark a promising stride in biometric synthesis, showcasing the potential of DDPMs to advance the landscape of fingerprint identification and authentication systems. |
| title | DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.04538 |