Analyzing the Effect of Combined Degradations on Face Recognition

Abstract: 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 https://github.com/ThEnded32/AnalyzingCombinedDegradations.