# On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detection

arXiv: https://arxiv.org/abs/2103.01721 · HTML: https://mlsanity.com/paper/2103.01721/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 100 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 22 of 22)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [face anti-spoofing based on color texture analysis](https://mlsanity.com/paper/1511.06316/) (2015)
- [Learn Convolutional Neural Network for Face Anti-Spoofing](https://mlsanity.com/paper/1408.5601/) (2014)
- [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://mlsanity.com/paper/1907.04047/) (2019)
- [Deep Tree Learning for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/1904.02860/) (2019)
- [Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks](https://mlsanity.com/paper/2007.11457/) (2020)
- [Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach](https://mlsanity.com/paper/2006.02834/) (2020)
- [LivDet in Action - Fingerprint Liveness Detection Competition 2019](https://mlsanity.com/paper/1905.00639/) (2019)
- [Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier](https://mlsanity.com/paper/1901.03918/) (2019)
- [Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks](https://mlsanity.com/paper/1908.10163/) (2019)
- [Discriminative Representation Combinations for Accurate Face Spoofing Detection](https://mlsanity.com/paper/1808.08802/) (2018)
- [Improving Face Anti-Spoofing by 3D Virtual Synthesis](https://mlsanity.com/paper/1901.00488/) (2019)
- [Detection of Makeup Presentation Attacks based on Deep Face Representations](https://mlsanity.com/paper/2006.05074/) (2020)
- [A Deep Generative Model of Vowel Formant Typology](https://mlsanity.com/paper/1807.02745/) (2018)
- [FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning](https://mlsanity.com/paper/1902.02845/) (2019)

## Built on by (most cited first, 8 of 8)

- [Differential Anomaly Detection for Facial Images](https://mlsanity.com/paper/2110.03464/) (2021)
- [SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2311.05336/) (2023)
- [Single Morphing Attack Detection using Siamese Network and Few-shot Learning](https://mlsanity.com/paper/2206.10969/) (2022)
- [Impact of Face Image Quality Estimation on Presentation Attack Detection](https://mlsanity.com/paper/2209.15489/) (2022)
- [Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection](https://mlsanity.com/paper/2211.07383/) (2022)
- [Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications](https://mlsanity.com/paper/2408.09558/) (2024)
- [In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems](https://mlsanity.com/paper/2507.15285/) (2025)
- [Detection of T-shirt Presentation Attacks in Face Recognition Systems](https://mlsanity.com/paper/2604.19365/) (2026)

## Abstract (arXiv)

In the last decades, the broad development experienced by biometric systems has unveiled several threats which may decrease their trustworthiness. Those are attack presentations which can be easily carried out by a non-authorised subject to gain access to the biometric system. In order to mitigate those security concerns, most face Presentation Attack Detection techniques have reported a good detection performance when they are evaluated on known Presentation Attack Instruments (PAI) and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are included in the test set. For those more realistic scenarios, the existing algorithms face difficulties to detect unknown PAI species in many cases. In this work, we use a new feature space based on Fisher Vectors, computed from compact Binarised Statistical Image Features histograms, which allow discovering semantic feature subsets from known samples in order to enhance the detection of unknown attacks. This new representation, evaluated for challenging unknown attacks taken from freely available facial databases, shows promising results: a BPCER100 under 17% together with an AUC over 98% can be achieved in the presence of unknown attacks. In addition, by training a limited number of parameters, our method is able to achieve state-of-the-art deep learning-based approaches for cross-dataset scenarios.