3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in finger-vein-recognition-4
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Use these libraries to find finger-vein-recognition-4 models and implementations
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A universal learning-based framework, which achieves generalization while training with limited data, and shows application potential in finger vein recognition and other vein-based biometric recognition as well.
A novel motion transfer model for finger vein image data augmentation via modeling the actual finger posture and rotational movements is proposed, demonstrating that the proposed motion transfer model can generate realistic intra-class augmented samples and effectively improve the recognition accuracy.
Adding a benchmark result helps the community track progress.