arXiv:2605. 20735v2 Announce Type: replace-cross Abstract: NIST Iris Exchange (IREX) offers an appealing solution to evaluating new open-source iris recognition algorithms, but it presents high barriers to entry because these algorithms must be written in C++, using a specific API, and adapted to meet strict IREX speed and memory constraints.
By Siamul Karim Khan, Patrick J. Flynn, Adam Czajka
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
arXiv:2607. 13905v1 Announce Type: cross Abstract: The International StepUP Competition Series was launched to advance research in pressure-based footstep biometrics through a standardized and challenging evaluation framework.
By Robyn Larracy, Anant Gupta, Gourav Gupta, Ethan Eddy, Maxime Devanne, Cyril Meyer, Jin-Chern Chiou, Yueh-Shan Lee, Zong-Han Lu, Aaron Tabor, Erik Scheme
Finger vein verification is a promising biometric modality for secure authentication because vascular patterns are internal, difficult to observe externally, and relatively resistant to presentation a...
Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed for natural images may disrupt the fine-grained topology and textures essential for identity discrimination.
ByteTraX is a lightweight enhancement to the ByteTrack multi‑object tracking architecture that introduces a single unified matching threshold and stricter track initiation criteria to reduce erroneous track reclassification and identity switches. The method yields consistent performance gains across several benchmarks—GMOT‑40, LC‑MOT, SportsMOT, TeamTrack, DAMUNT, and DeepSea‑MOT—while boosting processing speed by over 10%. Quantitatively, ByteTraX achieves more than a 40% drop in identity switches, with mean improvements of 3.6 in HOTA, 5.6 in IDF1, and 6.3 FPS.
By Thomas A. O'Shea-Wheller