arXiv:2607. 14932v1 Announce Type: cross Abstract: Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs.
Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace.
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:2611.07941v1 Announce Type: new Abstract: Large-scale identity-labeled face datasets are essential for training modern face recognition models, yet collecting and annotating such data is costly...
Large-scale identity-labeled face datasets are essential for training modern face recognition models, yet collecting and annotating such data is costly and raises privacy and ethical concerns. Existin...
arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.
arXiv:2608.23410v1 Announce Type: new Abstract: Photorealistic novel view synthesis of people remains challenging at high spatial resolutions and across multiple target cameras, where preserving iden...
arXiv:2609.27011v1 Announce Type: new Abstract: Target-oriented face de-identification models aim to anonymize the identity of a target individual across different images or video frames, such that t...
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.
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.
The paper introduces Ensemble Feedback Controllable Synthesis (EFCS), a framework that generates diverse, challenging synthetic face images while preserving visual realism. EFCS improves distributional variability, achieving higher FID and KID scores than single‑feedback or random synthesis, yet maintains high precision. Models trained on EFCS data outperform baselines on multiple benchmarks, and the authors provide an analytical formulation linking perturbation difficulty, sample utility, and performance degradation to guide synthetic data complexity.
arXiv:2609.12668v1 Announce Type: new Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks...