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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/2511.08119 |
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| _version_ | 1866914150809075712 |
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| author | Maity, Arnab Manasa C, Pavan Kumar Ramachandra, Raghavendra |
| author_facet | Maity, Arnab Manasa C, Pavan Kumar Ramachandra, Raghavendra |
| contents | Latent fingerprint identification remains a challenging task due to low image quality, background noise, and partial impressions. In this work, we propose a novel identification approach called LatentPrintFormer. The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features from latent fingerprints. A spatial attention module is employed to emphasize high-quality ridge regions while suppressing background noise. The extracted features are fused and projected into a unified 512-dimensional embedding, and matching is performed using cosine similarity in a closed-set identification setting. Extensive experiments on two publicly available datasets demonstrate that LatentPrintFormer consistently outperforms three state-of-the-art latent fingerprint recognition techniques, achieving higher identification rates across Rank-10. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification Maity, Arnab Manasa C, Pavan Kumar Ramachandra, Raghavendra Computer Vision and Pattern Recognition Latent fingerprint identification remains a challenging task due to low image quality, background noise, and partial impressions. In this work, we propose a novel identification approach called LatentPrintFormer. The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features from latent fingerprints. A spatial attention module is employed to emphasize high-quality ridge regions while suppressing background noise. The extracted features are fused and projected into a unified 512-dimensional embedding, and matching is performed using cosine similarity in a closed-set identification setting. Extensive experiments on two publicly available datasets demonstrate that LatentPrintFormer consistently outperforms three state-of-the-art latent fingerprint recognition techniques, achieving higher identification rates across Rank-10. |
| title | LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.08119 |