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arXiv:2609.01014v1 Announce Type: new Abstract: Low-quality face recognition (LQFR) remains challenging due to the difficulty of matching degraded query (probe) images against low-quality (LQ) enroll...
The paper introduces Dual Quality Margin Learning for Face Recognition (DQM‑Face), a framework that combines magnitude‑based and semantic quality estimation to refine attraction and repulsion dynamics during training. By integrating squeeze‑and‑excitation semantic attention with dual margins, the method enhances intra‑class compactness and inter‑class separation, yielding a more discriminative feature geometry. Experiments on challenging benchmarks show that DQM‑Face outperforms state‑of‑the‑art face recognition models and that the learned quality signal aligns well with recognition objectives.
The paper introduces ViT‑HarDis, a method that conditions attention head disentanglement on sample difficulty in Vision Transformers for face recognition. By applying stronger head decorrelation to hard samples—those with high cosine similarity to negative class centers—the approach encourages specialization where identity ambiguity is greatest. Experiments on WebFace4M and MS1MV3 show that ViT‑HarDis improves over baseline and uniform disentanglement, achieving state‑of‑the‑art results on IJB‑B, IJB‑C, and TinyFace without architectural changes or extra inference cost.
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.
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.
DenseFace introduces a bias‑mitigation technique for face recognition that operates on pre‑trained models without retraining. It models each person’s face embeddings with a von Mises‑Fisher distribution and uses a density‑aware probabilistic matching procedure to account for demographic differences. Experiments show that DenseFace consistently reduces racial bias across various architectures and datasets while preserving recognition accuracy.