A face recognition model is typically trained on large datasets of images
that may be collected from controlled environments. This results in performance
discrepancies when applied to real-world scenarios due to the domain gap
between clean and in-the-wild images. Therefore, some researchers have
investigated the robustness of these models by analyzing synthetic
degradations. Yet, existing studies have mostly focused on single degradation
factors, which may not fully capture the complexity of real-world degradations.
This work addresses this problem by analyzing the impact of both single and
combined degradations using a real-world degradation pipeline extended with
under/over-exposure conditions. We use the LFW dataset for our experiments and
assess the model's performance based on verification accuracy. Results reveal
that single and combined degradations show dissimilar model behavior. The
combined effect of degradation significantly lowers performance even if its
single effect is negligible. This work emphasizes the importance of accounting
for real-world complexity to assess the robustness of face recognition models
in real-world settings. The code is publicly available at
this https URL

分析面部识别模型在真实场景中的鲁棒性，研究表明合成退化会显著降低模型性能，强调了考虑真实世界复杂性的重要性。