---
# System prepended metadata

title: 'Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal'
tags: [生物辨識]

---

---
tags: 生物辨識
---
# Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal
制定一個統一的標準去評估人臉防偽模型的泛化性
> 現在大家的方法幾乎都會綁定到特定的資料集上面
- 設計實驗模擬真實場景。
- 
## Contributions
- Largest dataset
    - anti-spoofing: attacks, lighting, capture devices and resolution.
- We release an open-source evaluation framework.
- We demonstrate the limitation of current dataset evaluation procedures.
- We introduce two novel protocols for the GPAD problem.
    1. Cross-FaceResolution
    2. Cross-Conditions

## Generalized Presentation Attack Detection
Regardless almost every proposal comes with its own reduced dataset, there is no agreement upon a PAD benchmark, and generalization properties are not properly evaluated
> 現在最直接的做法都推薦直接採用不同dataset去驗證泛化性


![](https://i.imgur.com/ZUH90Ex.png)

- Stage1: Feature Extraction
- Stage2: Evaluation

> PAI: Presentation Attack Instruments

- The Aggregated Dataset

![](https://i.imgur.com/9xIWVoC.png)


- Categorization
> 對各種 attck/capture devices/lighting/face resolution 去做額外的分類。

![](https://i.imgur.com/vijIhVI.png)

- Protocols（考慮到各種情境）
    1. Grandtest (without any filter)
    2. Cross-Dataset (直接測試在不同的資料集)
    3. One-PAI
    4. Unseen Attacks (Cross-PAI)
    5. Unseen Capture Devices
    6. **Cross-FaceResolution**：高解析度訓練，低解析度測試
    7. **Cross-Conditions**：在有利的情況以及不利的情況下去做比較

![](https://i.imgur.com/uLiEIqV.png)

## 連結
- [Paper](https://arxiv.org/pdf/1904.06213.pdf)
- [Github](https://github.com/acostapazo/gradgpad)
