# Image Quality Assessment of Identity Cards Using Measures from Open Face Image Quality

arXiv: https://arxiv.org/abs/2606.11884 · HTML: https://mlsanity.com/paper/2606.11884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Considerations on the Evaluation of Biometric Quality Assessment Algorithms](https://mlsanity.com/paper/2303.13294/) (2023)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Second Competition on Presentation Attack Detection on ID Card](https://mlsanity.com/paper/2507.20404/) (2025)
- [State of the Art of Quality Assessment of Facial Images](https://mlsanity.com/paper/2211.08030/) (2022)
- [IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering](https://mlsanity.com/paper/2403.00573/) (2024)
- [Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization](https://mlsanity.com/paper/1602.02434/) (2016)
- [Identity Card Presentation Attack Detection: A Systematic Review](https://mlsanity.com/paper/2511.06056/) (2025)

## Abstract (arXiv)

This paper addresses the challenge of assessing image quality in ID cards in remote verification systems by applying capture-related quality measures from the Open Face Image Quality (OFIQ) standard to ID card images. Our preprocessing pipeline includes corner detection, perspective normalization, and comprehensive foreground masking to ensure accurate and unbiased quality measure computation. We evaluate the effectiveness of these measures by analyzing their correlation with the performance of three presentation attack detection (PAD) algorithms across four diverse ID card datasets, where two datasets contain bona fide, i.e. pristine, images and two contain printed mock ID cards. Our results suggest that quality assessment based on some OFIQ measures can significantly improve PAD performance.