# Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?

arXiv: https://arxiv.org/abs/2307.01845 · HTML: https://mlsanity.com/paper/2307.01845/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 4 Jul 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Presentation Attack Detection with Advanced CNN Models for Noncontact-based Fingerprint Systems](https://mlsanity.com/paper/2303.05459/) (2023)
- [Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network](https://mlsanity.com/paper/2212.05884/) (2022)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)

## Built on by (most cited first, 5 of 5)

- [A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems](https://mlsanity.com/paper/2310.15044/) (2023)
- [Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach](https://mlsanity.com/paper/2311.04148/) (2023)
- [ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces](https://mlsanity.com/paper/2503.05247/) (2025)
- [GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2504.01213/) (2025)
- [Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models](https://mlsanity.com/paper/2409.18636/) (2024)

## Abstract (arXiv)

The rapid evolution of high-end smartphones with advanced high-resolution cameras has resulted in contactless capture of fingerprint biometrics that are more reliable and suitable for verification. Similar to other biometric systems, contactless fingerprint-verification systems are vulnerable to presentation attacks. In this paper, we present a comparative study on the generalizability of seven different pre-trained Convolutional Neural Networks (CNN) and a Vision Transformer (ViT) to reliably detect presentation attacks. Extensive experiments were carried out on publicly available smartphone-based presentation attack datasets using four different Presentation Attack Instruments (PAI). The detection performance of the eighth deep feature technique was evaluated using the leave-one-out protocol to benchmark the generalization performance for unseen PAI. The obtained results indicated the best generalization performance with the ResNet50 CNN.