# Impact of Face Image Quality Estimation on Presentation Attack Detection

arXiv: https://arxiv.org/abs/2209.15489 · HTML: https://mlsanity.com/paper/2209.15489/

## 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 (most cited first, 18 of 18)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness](https://mlsanity.com/paper/2003.09373/) (2020)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing](https://mlsanity.com/paper/2011.02062/) (2020)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance](https://mlsanity.com/paper/2103.05977/) (2021)
- [On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/2011.08019/) (2020)
- [A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices](https://mlsanity.com/paper/2010.04145/) (2020)
- [Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks](https://mlsanity.com/paper/1908.10163/) (2019)
- [Iris Liveness Detection using a Cascade of Dedicated Deep Learning Networks](https://mlsanity.com/paper/2105.14009/) (2021)
- [On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detection](https://mlsanity.com/paper/2103.01721/) (2021)
- [Selfie Periocular Verification using an Efficient Super-Resolution Approach](https://mlsanity.com/paper/2102.08449/) (2021)

## Built on by (most cited first, 2 of 2)

- [Surveillance Face Anti-spoofing](https://mlsanity.com/paper/2301.00975/) (2023)
- [Radial Distortion in Face Images: Detection and Impact](https://mlsanity.com/paper/2501.07179/) (2025)

## 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.