# How far did we get in face spoofing detection?

arXiv: https://arxiv.org/abs/1710.09868 · HTML: https://mlsanity.com/paper/1710.09868/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2017.

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

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning](https://mlsanity.com/paper/2501.08799/) (2025)
- [Sejong Face Database: A Multi-Modal Disguise Face Database](https://mlsanity.com/paper/2106.07186/) (2021)
- [Understanding Cross Domain Presentation Attack Detection for Visible Face Recognition](https://mlsanity.com/paper/2111.02548/) (2021)

## Abstract (arXiv)

The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings the temporal evolution of the face spoofing detection field, as well as a comparative analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe trends in the existing approaches, to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection.