# Improving Presentation Attack Detection for ID Cards on Remote Verification Systems

arXiv: https://arxiv.org/abs/2301.09542 · HTML: https://mlsanity.com/paper/2301.09542/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 23 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://mlsanity.com/paper/1807.05786/) (2018)
- [Synthetic ID Card Image Generation for Improving Presentation Attack Detection](https://mlsanity.com/paper/2211.00098/) (2022)
- [The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF](https://mlsanity.com/paper/1304.2865/) (2013)
- [Large-scale Bisample Learning on ID Versus Spot Face Recognition](https://mlsanity.com/paper/1806.03018/) (2018)
- [DocFace: Matching ID Document Photos to Selfies](https://mlsanity.com/paper/1805.02283/) (2018)
- [DocFace+: ID Document to Selfie Matching](https://mlsanity.com/paper/1809.05620/) (2018)
- [Identity Document to Selfie Face Matching Across Adolescence](https://mlsanity.com/paper/1912.10021/) (2019)
- [Towards an Efficient Semantic Segmentation Method of ID Cards for Verification Systems](https://mlsanity.com/paper/2111.12764/) (2021)

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

- [First Competition on Presentation Attack Detection on ID Card](https://mlsanity.com/paper/2409.00372/) (2024)
- [Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries](https://mlsanity.com/paper/2409.06842/) (2024)

## Abstract (arXiv)

In this paper, an updated two-stage, end-to-end Presentation Attack Detection method for remote biometric verification systems of ID cards, based on MobileNetV2, is presented. Several presentation attack species such as printed, display, composite (based on cropped and spliced areas), plastic (PVC), and synthetic ID card images using different capture sources are used. This proposal was developed using a database consisting of 190.000 real case Chilean ID card images with the support of a third-party company. Also, a new framework called PyPAD, used to estimate multi-class metrics compliant with the ISO/IEC 30107-3 standard was developed, and will be made available for research purposes. Our method is trained on two convolutional neural networks separately, reaching BPCERtextsubscript100 scores on ID cards attacks of 1.69% and 2.36% respectively. The two-stage method using both models together can reach a BPCERtextsubscript100 score of 0.92%.