# Exploiting temporal and depth information for multi-frame face anti-spoofing

arXiv: https://arxiv.org/abs/1811.05118 · HTML: https://mlsanity.com/paper/1811.05118/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 10 of 10)

- [Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection](https://mlsanity.com/paper/2109.07950/) (2021)
- [Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition](https://mlsanity.com/paper/2103.13851/) (2021)
- [RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection](https://mlsanity.com/paper/2307.10642/) (2023)
- [Benchmarking Vision Foundation Models for Domain-Generalizable Face Anti-Spoofing](https://mlsanity.com/paper/2604.19196/) (2026)
- [Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing](https://mlsanity.com/paper/2401.08275/) (2024)
- [Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing](https://mlsanity.com/paper/2108.04032/) (2021)
- [CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection](https://mlsanity.com/paper/2607.01303/) (2026)
- [Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection](https://mlsanity.com/paper/2102.06864/) (2021)
- [Improved Detection of Face Presentation Attacks Using Image Decomposition](https://mlsanity.com/paper/2103.12201/) (2021)
- [Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing](https://mlsanity.com/paper/2208.04076/) (2022)

## Abstract (arXiv)

Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated recurrent units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW, CASIA-MFSD, and Replay-Attack.