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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15105 |
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| _version_ | 1866909586474139648 |
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| author | Wang, Yurun Qi, Zerong Fu, Shujun Hu, Mingzheng |
| author_facet | Wang, Yurun Qi, Zerong Fu, Shujun Hu, Mingzheng |
| contents | Latent fingerprint enhancement is a critical step in the process of latent fingerprint identification. Existing deep learning-based enhancement methods still fall short of practical application requirements, particularly in restoring low-quality fingerprint regions. Recognizing that different regions of latent fingerprints require distinct enhancement strategies, we propose a Triple Branch Spatial Fusion Network (TBSFNet), which simultaneously enhances different regions of the image using tailored strategies. Furthermore, to improve the generalization capability of the network, we integrate orientation field and minutiae-related modules into TBSFNet and introduce a Multi-Level Feature Guidance Network (MLFGNet). Experimental results on the MOLF and MUST datasets demonstrate that MLFGNet outperforms existing enhancement algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae Wang, Yurun Qi, Zerong Fu, Shujun Hu, Mingzheng Computer Vision and Pattern Recognition Artificial Intelligence Latent fingerprint enhancement is a critical step in the process of latent fingerprint identification. Existing deep learning-based enhancement methods still fall short of practical application requirements, particularly in restoring low-quality fingerprint regions. Recognizing that different regions of latent fingerprints require distinct enhancement strategies, we propose a Triple Branch Spatial Fusion Network (TBSFNet), which simultaneously enhances different regions of the image using tailored strategies. Furthermore, to improve the generalization capability of the network, we integrate orientation field and minutiae-related modules into TBSFNet and introduce a Multi-Level Feature Guidance Network (MLFGNet). Experimental results on the MOLF and MUST datasets demonstrate that MLFGNet outperforms existing enhancement algorithms. |
| title | A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15105 |