Summary of Gallery-aware Uncertainty Estimation For Open-set Face Recognition, by Leonid Erlygin et al.
Gallery-Aware Uncertainty Estimation For Open-Set Face Recognition
by Leonid Erlygin, Alexey Zaytsev
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to estimate image quality and model robustness improvement in unconstrained face recognition, specifically addressing the underexplored open-set face recognition task. It introduces a Bayesian probabilistic model of embedding distribution that provides a principled uncertainty estimate by considering two sources of ambiguity: gallery uncertainty caused by overlapping classes and face embeddings’ uncertainty. This method is evaluated on challenging datasets such as IJB-C and outperforms traditional uncertainty estimation methods based solely on image quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how to recognize faces that might be unknown, a challenge in computer vision. It suggests a new way to measure the uncertainty of face recognition, taking into account two types of uncertainty: when the gallery (a collection of known faces) is unclear and when the face itself is uncertain. This approach shows promise for identifying mistakes in face recognition. |
Keywords
» Artificial intelligence » Embedding » Face recognition » Probabilistic model
