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: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:2606. 24586v1 Announce Type: cross Abstract: Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary evaluation metric, typically the Equal Error Rate (EER).
The paper introduces BIND, a framework that binds a human’s biometric data to an AI agent’s identity and task-specific authority, enabling secure, real‑time delegation of control. By generating a token that an AI agent presents to an Identity Auditor, the system performs biometric authentication and recovers the agent’s ID and scope, providing non‑repudiable proof of human oversight. A practical implementation using face features and a fuzzy commitment scheme with turbo error‑correcting codes achieves a 96% true match rate at zero false match rate and supports 1024‑bit agent tokens.
arXiv:2606. 00380v1 Announce Type: cross Abstract: Machine unlearning removes the influence of specific training data from a trained model without retraining it from scratch.
Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary evaluation metric, typically the Equal Error Rate (EER). This paper introduces EERLoss: a subdifferentiable, arbitrarily accurate approximation to EER for training deep biometric models.
arXiv:2407. 13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential.
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.
Acne vulgaris affects most adolescents and many adults. Accurate severity grading guides treatment, monitoring, and clinical trial endpoints, but manual assessment using the Investigator's Global Assessment or Hayashi criteria is limited by inter-rater variability and inconsistent imaging conditions.
arXiv:2609.01027v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need ima...
arXiv:2609.01511v1 Announce Type: new Abstract: Face forgery detectors often achieve strong results on controlled benchmarks, but their reliability under realistic image degradations remains limited....
arXiv:2608.31094v1 Announce Type: new Abstract: Patient identity errors can compromise longitudinal medical records, research databases, and downstream clinical decisions. We present a retinal biomet...
arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.