# Face Liveness Detection Based on Client Identity Using Siamese Network

arXiv: https://arxiv.org/abs/1903.05369 · HTML: https://mlsanity.com/paper/1903.05369/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection](https://mlsanity.com/paper/2104.06148/) (2021)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)

## Abstract (arXiv)

Face liveness detection is an essential prerequisite for face recognition applications. Previous face liveness detection methods usually train a binary classifier to differentiate between a fake face and a real face before face recognition. The client identity information is not utilized in previous face liveness detection methods. However, in practical face recognition applications, face spoofing attacks are always aimed at a specific client, and the client identity information can provide useful clues for face liveness detection. In this paper, we propose a face liveness detection method based on the client identity using Siamese network. We detect face liveness after face recognition instead of before face recognition, that is, we detect face liveness with the client identity information. We train a Siamese network with image pairs. Each image pair consists of two real face images or one real and one fake face images. The face images in each pair come from a same client. Given a test face image, the face image is firstly recognized by face recognition system, then the real face image of the identified client is retrieved to help the face liveness detection. Experiment results demonstrate the effectiveness of our method.