ArticleScientific reports2025
A novel approach to palm vein image segmentation combining multi-scale convolution and swin-transformer networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Extreme Illuminated Vision Processing with a Graded Alloyed Perovskite In-sensor Computing Network.Nature communications · 2026Article
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3 authors.
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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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