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Topology-Aware Global-Local Mamba Networks for Palm Vein Biometrics

Palm-vein recognition is a fine-grained biometric task in which both local vascular texture and the global layout of the vessel tree carry discriminative information, while public datasets remain limited. We propose a topology-aware global-local backbone that combines multi-scale local features, a structureguided directional stream built on a fixed Sobel-magnitude edge prior, and a four-direction state-space scan global pathway within six Topology-Aware Blocks.

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arXiv Computer Vision
Aug 27

OpenVeinNet: Robust Open-Set Finger Vein Verification with Dynamic Snake Convolution and Graph Learning

OpenVeinNet is a finger vein verification framework that tackles open-set scenarios by combining Dynamic Snake Convolution, which extracts local curvilinear vein structures through adaptive sampling, with a graph convolutional backbone that models long-range topological relationships between vein regions. The model introduces a Centroid Angular Hybrid Loss to promote intra-class compactness and inter-class angular separation in the embedding space. Experiments on five public datasets under leave-one-dataset-out training demonstrate strong cross-dataset generalisation, low equal error rates, and competitive true accept rates at fixed false accept rates.

By Sushrut Patwardhan, Raghavendra Ramachandra
ragbenchmarks
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arXiv Computer Vision
Sep 3

Video-Based Palm-Vein Authentication under Challenging Conditions

The paper introduces the Columbia University Palm‑vein (CUP) dataset, the first public video‑based palm‑vein dataset that captures palms under four surface conditions—clean, warm, wet, and dirty—along with physiological and demographic metadata. Twenty‑one recognizers are benchmarked on CUP, revealing that models performing well on clean palms lose most accuracy on dirty palms, with mean EER roughly quadrupling. The authors propose a lightweight design that fuses global cosine similarity with a saliency‑steered region‑level optimal transport, achieving state‑of‑the‑art performance across all surfaces while reducing parameters and computational cost, and they identify demographic gaps in warm‑condition performance.

By Xiaofeng Yan, Kechen Liu, Abhilash Venkatesh, Cathy Zhang, Xia Zhou, Salvatore Stolfo
benchmarks
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arXiv Computer Vision
Aug 31

FVeinSyn: Synthetic Finger Vein Image Generator

FVeinSyn is a large‑scale synthetic finger‑vein image generator that separates vascular topology synthesis from imaging appearance rendering. It uses stochastic L‑systems to create anatomically valid, identity‑distinctive vein patterns, a cascaded region‑aware GAN to produce realistic near‑infrared images, and an intra‑class diversity generator to simulate realistic variations. The framework generated 500,000 images across 10,000 identities, and models trained with this data outperformed real‑data‑only baselines on eight public datasets, improving average accuracy by 27.43%.

By Yifan Wang, Jie Gui, Adams Wai Kin Kong, Baosheng Yu, Changsheng Chen, Qi Li, Zhenan Sun, James Tin-Yau Kwok, Alex Kot
diffusion
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