# Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing

arXiv: https://arxiv.org/abs/1901.05602 · HTML: https://mlsanity.com/paper/1901.05602/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2019.

## Built on by (most cited first, 16 of 16)

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Multi-domain Learning for Updating Face Anti-spoofing Models](https://mlsanity.com/paper/2208.11148/) (2022)
- [Joint Face Image Restoration and Frontalization for Recognition](https://mlsanity.com/paper/2105.09907/) (2021)
- [Image-to-Video Generation via 3D Facial Dynamics](https://mlsanity.com/paper/2105.14678/) (2021)
- [One-Class Knowledge Distillation for Face Presentation Attack Detection](https://mlsanity.com/paper/2205.03792/) (2022)
- [Novelty Detection via Contrastive Learning with Negative Data Augmentation](https://mlsanity.com/paper/2106.09958/) (2021)
- [Camera Invariant Feature Learning for Generalized Face Anti-spoofing](https://mlsanity.com/paper/2101.10075/) (2021)
- [Adversarial Attacks on Video Object Segmentation with Hard Region Discovery](https://mlsanity.com/paper/2309.13857/) (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)
- [AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Deepfake Detection of Frontal Face Videos](https://mlsanity.com/paper/2311.02733/) (2023)
- [STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario](https://mlsanity.com/paper/2404.00362/) (2024)
- [Fast and Reliable Probabilistic Face Embeddings in the Wild](https://mlsanity.com/paper/2102.04075/) (2021)
- [Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning](https://mlsanity.com/paper/2206.06510/) (2022)
- [IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images](https://mlsanity.com/paper/2309.17399/) (2023)
- [A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection](https://mlsanity.com/paper/2212.06958/) (2022)

## Abstract (arXiv)

Face anti-spoofing (a.k.a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems. Existing CNN-based approaches usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of unseen scenes. In this paper, we try to boost the generalizability and applicability of these methods by designing a CNN model with two major novelties. First, we propose a simple yet effective Total Pairwise Confusion (TPC) loss for CNN training, which enhances the generalizability of the learned Presentation Attack (PA) representations. Secondly, we incorporate a Fast Domain Adaptation (FDA) component into the CNN model to alleviate negative effects brought by domain changes. Besides, our proposed model, which is named Generalizable Face Authentication CNN (GFA-CNN), works in a multi-task manner, performing face anti-spoofing and face recognition simultaneously. Experimental results show that GFA-CNN outperforms previous face anti-spoofing approaches and also well preserves the identity information of input face images.