mlsanityData to 14 Oct 2026

Impact of Face Image Quality Estimation on Presentation Attack Detection

arXiv 2209.15489 ↗First posted 30 Sep 2022cs.CVcited by 2

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Abstract (arXiv)

Non-referential face image quality assessment methods have gained popularity as a pre-filtering step on face recognition systems. In most of them, the quality score is usually designed with face matching in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Attack Detection (PAD). In this paper, we study the effect of quality assessment methods on filtering bona fide and attack samples, their impact on PAD systems, and how the performance of such systems is improved when training on a filtered (by quality) dataset. On a Vision Transformer PAD algorithm, a reduction of 20% of the training dataset by removing lower quality samples allowed us to improve the BPCER by 3% in a cross-dataset test.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

18 of 37 references.

References · 18

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6 more
  1. 2020On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofingcited by 44
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023Surveillance Face Anti-spoofingcited by 12
  2. 2025Radial Distortion in Face Images: Detection and Impactcited by 0