# Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

arXiv: https://arxiv.org/abs/1410.1980 · HTML: https://mlsanity.com/paper/1410.1980/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2014.

## Built on by (most cited first, 21 of 21)

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Unsupervised Compound Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2105.08463/) (2021)
- [Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising](https://mlsanity.com/paper/2111.07620/) (2021)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2412.07199/) (2024)
- [Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances](https://mlsanity.com/paper/2111.12465/) (2021)
- [EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images](https://mlsanity.com/paper/2112.11610/) (2021)
- [Fingerprint Presentation Attack Detector Using Global-Local Model](https://mlsanity.com/paper/2402.12754/) (2024)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images](https://mlsanity.com/paper/2309.17399/) (2023)
- [PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics](https://mlsanity.com/paper/2310.02140/) (2023)
- [Adversarial Masking Contrastive Learning for vein recognition](https://mlsanity.com/paper/2401.08079/) (2024)
- [AutoSIGHT: Automatic Eye Tracking-based System for Immediate Grading of Human experTise](https://mlsanity.com/paper/2508.01015/) (2025)
- [DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2111.00919/) (2021)
- [Mitigating Presentation Attack using DCGAN and Deep CNN](https://mlsanity.com/paper/2207.00161/) (2022)
- [Presentation Attack Detection using Convolutional Neural Networks and Local Binary Patterns](https://mlsanity.com/paper/2312.00041/) (2023)
- [Advancing Ear Biometrics: Enhancing Accuracy and Robustness through Deep Learning](https://mlsanity.com/paper/2406.00135/) (2024)
- [MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification](https://mlsanity.com/paper/2408.10694/) (2024)
- [Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach](https://mlsanity.com/paper/2608.08977/) (2026)

## Abstract (arXiv)

Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks --- each one containing real and fake samples of a given biometric modality and attack type --- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.