This is the second large-scale kinship recognition data competition, in conjunction with FG 2018. This is made possible with the release of the largest and most comprehensive image database for automatic kinship recognition, Families in the Wild (FIW).
2018 RFIW will support 2 laboratory style evaluation protocols.
To learn more, participate, and download data individuals & teams register via CodaLab: Kinship Verification, Family Classification, Tri-Subject Verification
For more information on 2018 FG, see https://fg2018.cse.sc.edu/.
For more information on the database, see the FIW homepage: http://smile-fiw.weebly.com/.
To look back at RFIW2017 visit https://web.northeastern.edu/smilelab/RFIW2017/
Check out our kinship recognition toolbox on Github!
Best paper award will be awarded to top performing team (in terms of results and presentation)
2018 RFIW will support 2 laboratory style evaluation protocols.
- Kinship Verification (one-to-one)
- Family Classification (one-to-many)
- (one-to-two)
To learn more, participate, and download data individuals & teams register via CodaLab: Kinship Verification, Family Classification, Tri-Subject Verification
For more information on 2018 FG, see https://fg2018.cse.sc.edu/.
For more information on the database, see the FIW homepage: http://smile-fiw.weebly.com/.
To look back at RFIW2017 visit https://web.northeastern.edu/smilelab/RFIW2017/
Check out our kinship recognition toolbox on Github!
Best paper award will be awarded to top performing team (in terms of results and presentation)
Challenge Overview
Automatic kinship recognition hold promises to an abundance of applications. For starters, aiding forensic investigations– kinship is a powerful cue that would certainly narrow the search space (e.g., perhaps knowledge that the “Boston Bombers” were brothers could have helped identify the suspects sooner). In short, there are many beneficiaries that could result from such technologies: whether the consumer (e.g., automatic photo library management), scholar (e.g., historic lineage & genealogical studies), data analyzer (e.g., social-media-based analysis), or investigator (e.g., cases of missing children and human trafficking– for instance, it is unlikely that a missing child found online would be in any database, however, more than likely a family member would be. Besides application-based problems, and as already hinted, kinship is a powerful cue that could serve as a face attribute capable of greatly reducing the search space in more general face-recognition problems. With our FIW database, we can pose this relatively new and challenging problem at sizes much greater than ever before (e.g., kinship verification with 644,000 face pairs, opposed to just 2,000 and family classification with 1,000 families opposed to just 101. In the end, we also hope FIW serves as a rich resource to further bridge the semantic gap of facial recognition based problems to the broader human-computer interaction incentive.
A fair question to ask– if so useful, why is kinship recognition capable not found or even prototyped for any real-world problem? I mean, great efforts have been spent to advance automatic kinship recognition technologies dating back to 2010. We found the reasoning to be 2- fold:
- Existing image datasets for kinship recognition tasks are not large enough to capture and reflect the true data distributions of the families of the world.
- Kin-based relationships in the visual domain are less discriminant than other, more conventional problems (e.g., facial recognition or object classification), as there exists many hidden factors that affect the facial appearances amongst different family members
Both points were addressed with the introduction of our FIW database, with data distributions to properly represent real world scenarios available at scales much larger than ever before. We expect the larger, more complex data will pave way to modern day data driven methods (i.e., deep learning) to be used in these problems much more effectively than before possible.
Important Dates
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Contact Us
Joseph Robinson (robinson.jo@husky.neu.edu) and Ming Shao (mshao@umassd.edu)
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
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