隨著人臉識別被廣泛運用在各種安全關鍵應用中，Face Anti-Spoofing(FAS)
的相關研究也越來越受關注。FAS 在保護人臉識別系統免於受到 Presentation
Attacks(PAs) 的方面，有著至關重要的作用。然而，很少有 FAS 方法參考人
臉圖片質量，並且很少考慮在不受約束的場景下捕獲足夠高質量圖像時遇到
的困難。以前的方法沒有考慮對 inputed frame 計算質量分數，也沒有根據圖
像質量對不同 frame 應用不同的權重來做出反欺騙的最終決策，因此未能利
用高質量的圖像做出更準確的預測結果。為了解決這個問題，我們開發了基
於人臉圖像質量評估的人臉反欺騙系統。預期的貢獻是，在提取特徵向量
時，質量評估模塊生成輸入圖像的質量分數，透過更依賴質量好的資料，更
好地去區分真人臉圖像和假人臉圖像。我們通過對兩個公共數據集的實驗比
較，來展示所提出的基於 FIQA 的 FAS 系統的表現。

With the widespread use of face recognition in various security-critical applications,
research on face anti-spoofing (FAS) has attracted increasing attention. Face antispoofing plays a vital role in protecting face recognition systems from PAs. However, there are few FAS methods that refer to face image quality and rarely take
into account the difficulties encountered in capturing high-quality images under unconstrained scenarios. Previous methods do not consider computing quality scores
to the input frames or apply different weighting to different frames based on the
image quality to make the final decision for anti-spoofing, thus they failed to exploit the high-quality images to make more accurate prediction result. To solve this
problem, we developed a Face Image Quality Assessment (FIQA) based face antispoofing system. The expected contribution is that while extracting feature vectors,
the quality assessment module generated the quality scores for the input images to
make better decisions on distinguishing real face images from fake face images. We
demonstrate the proposed FIQA-based face anti-spoofing system through experimental comparisons on two public datasets.

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Related Work 5
2.1 Face Anti-Spoofing . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Face Image Quality Assessment . . . . . . . . . . . . . . . . . . . 6
3 Proposed Method 9
3.1 Two Modules of FIQA-FAS . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 Module of Face Anti-Spoofing . . . . . . . . . . . . . . . . 9
3.1.2 Module of Face Image Quality Assessment . . . . . . . . . 10
3.2 Loss function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Training Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4 Experimental Results 15
4.1 Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.1 Protocol of SiW . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.2 Protocol of SiW-M . . . . . . . . . . . . . . . . . . . . . . 16
4.1.3 Database with Simulated Scenarios . . . . . . . . . . . . . 16
4.2 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3 Testing Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3.1 Weighted Score Decision . . . . . . . . . . . . . . . . . . . 19
4.3.2 Error Rate Metrics . . . . . . . . . . . . . . . . . . . . . . 20
4.3.3 Experiment on SiW . . . . . . . . . . . . . . . . . . . . . . 20
4.3.4 Experiments on SiW-M . . . . . . . . . . . . . . . . . . . . 21
5 Ablation study 24
6 Conclusions 26
7 Future Research Direction 27
References 28

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