# Analyzing the Effect of Combined Degradations on Face Recognition

arXiv: https://arxiv.org/abs/2406.02142 · HTML: https://mlsanity.com/paper/2406.02142/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition](https://mlsanity.com/paper/1606.02894/) (2016)
- [How Image Degradations Affect Deep CNN-based Face Recognition?](https://mlsanity.com/paper/1608.05246/) (2016)
- [Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations](https://mlsanity.com/paper/1710.01494/) (2017)
- [On Recognizing Occluded Faces in the Wild](https://mlsanity.com/paper/2109.03672/) (2021)
- [PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement](https://mlsanity.com/paper/2210.00712/) (2022)
- [Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition](https://mlsanity.com/paper/2211.15225/) (2022)
- [Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation](https://mlsanity.com/paper/2303.08665/) (2023)

## Built on by (most cited first, 2 of 2)

- [Impact of Face Alignment on Face Image Quality](https://mlsanity.com/paper/2412.11779/) (2024)
- [Employing Vision-Language Models for Face Image Quality Assessment](https://mlsanity.com/paper/2605.17489/) (2026)

## Abstract (arXiv)

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.