Summary of Towards Visual Saliency Explanations Of Face Verification, by Yuhang Lu et al.
Towards Visual Saliency Explanations of Face Verification
by Yuhang Lu, Zewei Xu, Touradj Ebrahimi
First submitted to arxiv on: 15 May 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Image and Video Processing (eess.IV)
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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 Deep convolutional neural networks have revolutionized face recognition (FR) techniques, achieving high accuracy in both verification and identification scenarios. However, their lack of explainability has sparked criticism. To address this, researchers have explored visual saliency maps as an explanation method, but a comprehensive analysis in the context of FR is lacking. This paper focuses on explainable face verification tasks, proposing a new framework that combines a definition of saliency-based explanations with a model-agnostic approach called CorrRISE, which generates saliency maps revealing both similar and dissimilar regions between face images. To evaluate the performance of visual saliency explanation methods in FR, a novel methodology is designed. Substantial results demonstrate that CorrRISE outperforms state-of-the-art approaches in explainable face verification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Face recognition (FR) technology has made huge progress using deep learning models. But these models are hard to understand. This paper tries to fix this by creating a new way to show why FR models make certain decisions. The method, called CorrRISE, creates maps that highlight what’s similar and different between two face images. This helps us understand how FR models work. The researchers also tested their method with real FR data and found it works well. |
Keywords
» Artificial intelligence » Deep learning » Face recognition
