mlsanityData to 13 Oct 2026

ARoFace: Alignment Robustness to Improve Low-Quality Face Recognition

arXiv 2407.14972 ↗First posted 20 Jul 2024cs.CVcited by 4

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Abstract (arXiv)

Aiming to enhance Face Recognition (FR) on Low-Quality (LQ) inputs, recent studies suggest incorporating synthetic LQ samples into training. Although promising, the quality factors that are considered in these works are general rather than FR-specific, eg, atmospheric turbulence, resolution, etc. Motivated by the observation of the vulnerability of current FR models to even small Face Alignment Errors (FAE) in LQ images, we present a simple yet effective method that considers FAE as another quality factor that is tailored to FR. We seek to improve LQ FR by enhancing FR models' robustness to FAE. To this aim, we formalize the problem as a combination of differentiable spatial transformations and adversarial data augmentation in FR. We perturb the alignment of the training samples using a controllable spatial transformation and enrich the training with samples expressing FAE. We demonstrate the benefits of the proposed method by conducting evaluations on IJB-B, IJB-C, IJB-S (+4.3% Rank1), and TinyFace (+2.63%). hrefhttps://github.com/msed-Ebrahimi/ARoFacehttps://github.com/msed-Ebrahimi/ARoFace

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 59 of its 87 references that are in the map. First posted on arXiv 20 Jul 2024.

Builds on

59 of 87 references.

References · 59

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 202550 Years of Automated Face Recognitioncited by 14
  2. 2024Impact of Face Alignment on Face Image Qualitycited by 1
  3. 2025DArFace: Deformation Aware Robustness for Low Quality Face Recognitioncited by 1
  4. 2026Low-Quality Face Recognition using Center Aligned Representations and Local Margin Constraintscited by 0