arXiv:2608. 08977v1 Announce Type: cross Abstract: This work focuses on the impact and detection of clear contact lenses in the context of iris recognition.
arXiv:2609.09188v1 Announce Type: new Abstract: Lensless near-eye sensing is often described as privacy-friendly because its coded measurements are visually unintelligible. Yet visual unintelligibili...
arXiv:2609.32352v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging r...
arXiv:2607. 03581v1 Announce Type: cross Abstract: The widespread adoption of facial masks, accelerated by COVID-19 and mandated in security-sensitive settings, has exposed limitations of conventional face recognition systems.
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.
arXiv:2607. 05825v1 Announce Type: cross Abstract: Background.
arXiv:2606. 08123v2 Announce Type: replace-cross Abstract: Model selection for safety-relevant visual recognition is often based on clean aggregate performance, although robustness, transfer, embedded latency, and explanation faithfulness may produce different preferences.
The paper evaluates three compact computer‑vision models—each with about 0.26 million trainable parameters—for iris presentation attack detection (PAD) under conditions not seen during training, such as new presentation attack instruments (PAIs) and environmental degradation. The models are trained from scratch without external pretraining or data augmentation and tested on the Notre Dame subset of LivDet‑Iris 2017 across known‑attack, unknown‑attack, corrupted, and pooled partitions. Results show significant performance drops when moving from known to unknown attacks, with the best model, ZACH‑ViT, achieving the lowest unknown‑attack APCER (≈47.7 %) and D‑EER (≈38.9 %), yet overall errors remain high enough to preclude deployment readiness.
arXiv:2608. 07651v1 Announce Type: new Abstract: Large language models (LLMs) show promise in medical image interpretation but suffer from hallucination, limited accuracy, and run-to-run inconsistency.
The paper introduces OcuBench, a comprehensive benchmark for eyeglass reflection removal that includes 10,280 synthetic pairs, 732 real-input pseudo-pairs, and 458 real-world test images, enabling both paired evaluation and assessment beyond generated supervision. It also proposes OcuFlow, an ocular-adaptive pixel MeanFlow framework that uses geometry-adaptive representation and one-step pMF to focus on reflection-obscured ocular regions while preserving native-resolution details. Experiments show OcuFlow consistently outperforms baselines in reflection removal quality, ocular fidelity, and efficiency, achieving 67.32% of selections in a blind user study, six times the next-best share.
OptiModNet is a lightweight UNet‑Transformer hybrid designed for optic disc and cup segmentation. It incorporates grouped‑query and channel attention across multiple stages, along with an Aggregated Pyramid Loss to improve gradient flow and structural consistency. Evaluated on the REFUGE2 dataset, it surpasses existing methods by over 2.5 % while using only 3.73 GFLOPs and 1.93 M parameters.
The paper introduces TSMSA-Net, a two‑stage multi‑scale attention‑based network designed for weakly supervised enhancement of cataract fundus images. It first synthesizes paired real‑like cataract images via domain transformation, then applies a multi‑scale attention stage to extract detailed features while minimizing artifacts. Experiments on Kaggle and ODIR‑5K datasets show that TSMSA‑Net surpasses current state‑of‑the‑art methods even without paired data and improves vessel segmentation and classification performance.
LENS‑GRF is a permutation‑invariant lesion evidence network that uses a Set‑Transformer and gated residual fusion to combine global facial context with localized lesion patches for four‑class acne severity grading. The framework integrates adaptive facial skin segmentation, a Vision Transformer prior, and a lesion set transformer that encodes spatial geometry, with a gating mechanism that modulates local residual contributions. In experiments on ACNE04 and PLSBRACNE01, the fully automated model achieved 80.82% accuracy, while using ground‑truth lesion annotations raised accuracy to 95.89% and a Quadratic Weighted Kappa of 0.9753; zero‑shot evaluation on the full cohort yielded 35.00% accuracy versus 42.50% for a global baseline, and oracle analyses on a 148‑subject cohort showed improved accuracy and QWK up to 47.97% and 0.5799.