FIQA-FAS: Face Image Quality Assessment Based Face Anti-Spoofing

Ya-Chi Liang, Min-Xuan Qiu, Shang-Hong Lai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1462-1470

Face anti-spoofing (FAS) is to protect facial recognition systems against presentation attacks. However recent research on FAS often neglects real-world conditions such as changing illumination varying angles of face and motion blur within a video. These conditions lead to inconsistent feature quality across face images where low-quality features can cause the model to learn unreliable information during training. Moreover frames with low feature quality within videos result in inaccurate decisions. To address this issue we propose the Face Image Quality Assessment Based Face Anti-Spoofing System (FIQA-FAS) which integrates a face image quality assessment module with a face anti-spoofing module. FIQA-FAS assesses the feature quality extracted from each face image and uses the quality score to compute a weighted prediction for deciding if the face in a video is live or spoof. We demonstrate the effectiveness of FIQA-FAS through experiments on the SIW and SIW-M datasets. To further demonstrate our model's capabilities we introduce a novel simulated scenario that mimics the real world where our model outperforms other SOTA.