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.
By Guruprasad Viswanathan Ramesh, Ashish Hooda, Shimaa Ahmed, Harrison J Rosenberg, Ramya Korlakai Vinayak, Kassem Fawaz
MidSurfNet is a learning-based method for pairing faces in thin‑walled CAD models, addressing the key subproblem of mid‑surface abstraction for finite element analysis. It uses a dual‑stream scorer—one geometry stream and one attributed‑topology stream—fused by a pair‑conditioned gate to make per‑pair decisions that replace manual thresholds. The method organizes retained relations into variable‑cardinality face groups independent of processing order, and on a new MidSurf dataset of 1,575 annotated models it achieves an 87.32% pair‑level F1‑score and a 75.42% completion rate, outperforming rule‑based baselines, especially on multi‑wall‑thickness cases.
By Li Ye, Xinhang Zhou, Xingyu Yang, Ruofeng Tong, Hailong Li, Peng Du, Min Tang
EXPL-FR is a lightweight adapter that aligns a vision‑language model’s image encoder with a frozen face‑recognition (FR) embedding space, enabling the FR model to be explained using semantic attribute prompts without any text training. By mapping 978 attribute prompts across 22 categories into the FR space, the method identifies the most detectable concepts—forming a readable semantic signature that better separates identities than the full vocabulary. The approach is evaluated on four FR backbones and two VLM encoders, providing identity‑level, per‑image, and differential explanations, and demonstrates that prompt‑driven audits can rank FR models by per‑ethnicity error and attribute‑change verification cost without requiring labeled data.
By Guray Ozgur, Mustafa Efe Tamyapar, Naser Damer, Fadi Boutros
The paper introduces a unified evaluation framework for face de-identification (FDeID), addressing inconsistencies in current protocols. It presents UtilFace, a demographically balanced benchmark assembled from four large-scale datasets, and HiFD, a hierarchical metric that simultaneously measures identity suppression, multi-level utility preservation, and image quality. Using this framework, the authors compare adversarial, GAN-based, and diffusion-based methods, revealing trade-offs and failure modes that were previously hidden, and they release the benchmark and toolkit for reproducible research.
By Hui Wei, Hao Yu, Hui Kuurila-Zhang, Guoying Zhao
The paper audits the MS1MV3 face‑recognition training dataset and identifies five defect categories—exact/near duplicates, split identities, crop errors, multiple‑face ambiguity, and invalid content. Using IResNet‑50 and ArcFace, the authors show that correcting these defects, especially duplicate removal, can maintain or improve recognition performance while reducing training effort. The study highlights that dataset quality significantly influences large‑scale face‑recognition outcomes.
By Ana Dias, Jo\~ao Ribeiro Pinto, Hugo Proen\c{c}a, Jo\~ao C. Neves
arXiv:2608.24430v1 Announce Type: new
Abstract: Responsible deployment of face verification systems requires more than accurate decisions: systems should also provide interpretable and auditable evid...
By Ana Estrada-Real, Lydia Alapatt, Christoph Busch, Christian Rathgeb