Towards Interpretable Face Recognition
Abstract
Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions. Recent works further push the interpretability in the network learning stage to learn more meaningful representations. In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition. We propose a spatial activation diversity loss to learn more structured face representations. By leveraging the structure, we further design a feature activation diversity loss to push the interpretable representations to be discriminative and robust to occlusions. We demonstrate on three face recognition benchmarks that our proposed method is able to achieve the state-of-art face recognition accuracy with easily interpretable face representations.
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Research has explored some potential directions to interpret face recognition procedures by enforcing internal features to express explainable structures.
“Research has explored some potential directions to interpret face recognition procedures [95, 87] by enforcing internal features to express explainable structures.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [95]
Cites
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Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612 · see citing works
Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M., & Seifert, C. (2023) · ACM Computing Surveys · doi:10.1145/3583558 · Cited by 590 · see citing works
Data Uncertainty Learning in Face Recognition
Chang, J., Lan, Z., Cheng, C., & Wei, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00575 · Cited by 320 · see citing works
- Authors
- Yin, B., Tran, L., Li, H., Shen, X., & Liu, X.
- Affiliations
- Michigan State University (US)
- ByteDance (China) (CN)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9347-9356
- OpenAlex
- W2989282534
- Cited by
- 90
- References
- 8
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 15 · 2023: 17 · 2022: 21 · 2021: 13 · 2020: 12 · 2019: 3
- FWCI
- 3.99
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- occlusion robustness, deep convolutional neural networks
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