# Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

arXiv: https://arxiv.org/abs/2307.02858 · HTML: https://mlsanity.com/paper/2307.02858/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Unsupervised Representation Learning by Sorting Sequences](https://mlsanity.com/paper/1708.01246/) (2017)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [Meta-Teacher For Face Anti-Spoofing](https://mlsanity.com/paper/2111.06638/) (2021)
- [Generative Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2207.10015/) (2022)
- [Learning Meta Pattern for Face Anti-Spoofing](https://mlsanity.com/paper/2110.06753/) (2021)
- [Restricted Black-box Adversarial Attack Against DeepFake Face Swapping](https://mlsanity.com/paper/2204.12347/) (2022)
- [Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing](https://mlsanity.com/paper/2112.14894/) (2021)
- [Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing](https://mlsanity.com/paper/1912.07124/) (2019)
- [Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection](https://mlsanity.com/paper/2301.02145/) (2023)
- [Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets](https://mlsanity.com/paper/2208.13070/) (2022)
- [Face Anti-Spoofing from the Perspective of Data Sampling](https://mlsanity.com/paper/2208.13164/) (2022)
- [Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing](https://mlsanity.com/paper/2108.04032/) (2021)

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

- [Saliency-based Video Summarization for Face Anti-spoofing](https://mlsanity.com/paper/2308.12364/) (2023)
- [Semi-Supervised learning for Face Anti-Spoofing using Apex frame](https://mlsanity.com/paper/2309.04958/) (2023)
- [Advancing Cross-Domain Generalizability in Face Anti-Spoofing: Insights, Design, and Metrics](https://mlsanity.com/paper/2406.12258/) (2024)

## Abstract (arXiv)

Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario.