The paper introduces SBMEX, a new skeleton‑based minutiae detection method that replaces legacy C/C++ code with a fast, deterministic algorithm integrated into the open‑source pyfing package. SBMEX uses a dual Look‑Up Table architecture to eliminate runtime neighborhood scanning during Crossing Number computation and skeleton tracking, and adds a continuous quality scoring framework based on tracking path length, dual ridge‑valley skeleton fusion, and spatial density decay. Evaluation on NIST SD302 shows that SBMEX matches or surpasses traditional open‑source baselines in accuracy while dramatically reducing detection latency compared to classical Python implementations.
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.
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.
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.
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.
The ByteTrack algorithm is a widely used and computationally efficient multi-object tracking architecture. Its core innovation lies in the combination of lenient bounding box associations with trackle...
arXiv:2605. 09089v2 Announce Type: replace-cross Abstract: Digital onboarding and eKYC systems used by banks, fintech platforms, telecom providers, and other third-party services commonly verify users by comparing an uploaded identity document with a selfie or live facial capture.
arXiv:2607. 06615v1 Announce Type: cross Abstract: Image forgery detection is a critical task in digital forensics, yet many deep-learning localization approaches are typically GPU-accelerated and computationally heavier than handcrafted screening methods.
The paper presents a new approach to automatic skeleton generation that tackles two key challenges: encoding branch structures into token sequences and leveraging test-time computation. It introduces branch‑centric tokenization, a representation that groups structurally related elements together and directly encodes connectivity, producing more compact sequences than traditional BFS serialization. Additionally, the authors propose view‑augmented generation, a test‑time augmentation technique that rotates the input mesh, aligns predictions, and selects the best skeleton based on mesh coverage and consistency across views, leading to improved accuracy on benchmark datasets.
arXiv:2607. 21776v1 Announce Type: new Abstract: Talking-face (TF) deepfake generation synthesizes photore- alistic facial video from a static source image and an au- dio signal, producing forgeries that current image-based detectors consistently fail to identify.
License plate character detection is a crucial component of intelligent transportation systems, where high accuracy and computational efficiency are required for real-time deployment. Although recent deep learning-based methods have substantially improved detection performance, many high-accuracy models rely on large-scale architectures that incur substantial computational overhead, limiting their applicability to resource-constrained devices.