Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge
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Abstract (arXiv)
Recognizing Families In the Wild (RFIW): an annual large-scale, multi-track automatic kinship recognition evaluation that supports various visual kin-based problems on scales much higher than ever before. Organized in conjunction with the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG) as a Challenge, RFIW provides a platform for publishing original work and the gathering of experts for a discussion of the next steps. This paper summarizes the supported tasks (i.e., kinship verification, tri-subject verification, and search & retrieval of missing children) in the evaluation protocols, which include the practical motivation, technical background, data splits, metrics, and benchmark results. Furthermore, top submissions (i.e., leader-board stats) are listed and reviewed as a high-level analysis on the state of the problem. In the end, the purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 15 Feb 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Facescited by 4
- 2022Confidence-Calibrated Face and Kinship Verificationcited by 1
- 2023KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learningcited by 1
- 2021Solving the Families In the Wild Kinship Verification Challenge by Program Synthesiscited by 0
- 2023KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning19 Sep 2023
- 2022Confidence-Calibrated Face and Kinship Verification25 Oct 2022
- 2021The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces31 Oct 2021
- 2021Solving the Families In the Wild Kinship Verification Challenge by Program Synthesis13 Oct 2021