A novel approach to palm vein image segmentation combining multi-scale convolution and swin-transformer networks
Abstract This paper proposes a non-contact palm vein image segmentation model that integrates multiscale convolution and Swin-Transformer. Based on an enhanced U-Net architecture, the downsampling path employs a multiscale convolution module to extract hierarchical features, while the upsampling pat...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-02757-7 |
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| Summary: | Abstract This paper proposes a non-contact palm vein image segmentation model that integrates multiscale convolution and Swin-Transformer. Based on an enhanced U-Net architecture, the downsampling path employs a multiscale convolution module to extract hierarchical features, while the upsampling path captures global vein distribution through a sliding window attention mechanism. A feature fusion module suppresses background interference by integrating cross-layer information. Experimental results demonstrate that the model achieves 97.8% accuracy and 94.5% Dice coefficient on the PolyU and CASIA datasets, with a 3.2% improvement over U-Net. Ablation studies validate the synergistic effectiveness of the proposed modules. The model effectively enhances the robustness of palm vein recognition in complex illumination and noisy environments. |
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| ISSN: | 2045-2322 |