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Explainable Face Recognition
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“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 [87]
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Cited by
Sorted by citations
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983
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
Metadata
- Authors
- Williford, J. R., May, B. B., & Byrne, J.
- Affiliations
- Systems and Technology Research (United States) (US)
- Corresponding author
- May, Brandon B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 248-263
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3047300400
- Cited by
- 76
- References
- 4
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 13 · 2023: 15 · 2022: 29 · 2021: 7
- FWCI
- 3.93
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- face matching, attention maps, benchmark
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