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.
By Parisa Farmanifard, Arun Ross
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).
By Nahuel Gonzalez, Marta Robledo-Moreno, Ivan DeAndres-Tame, Ruben Vera-Rodriguez, Ruben Tolosana
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.
By Joseph Geo Benjamin, Anil K Jain, Karthik Nandakumar
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.
By Raffaele Cappelli
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.
By Petros Andreou, Jamie Lanyon, Axel Finke, Georgina Cosma
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.