News

  • 2019-05-20 FIW to organize premiere Kaggle Competition [kaggle]
  • 2019-01-21 Tutorial on kinship recognition at 2019 CVPR
  • 2019-01-16 Tutorial on kinship recognition at 2019 FG
  • 2018-10-27 Presented tutorial on kinship recognition at 2018 ACM MM [    slides]
  • 2018-08-09 3rd Recognizing Families In the Wild (2018 RFIW) at Automatic Face & Gesture (2018 FG)
  • 2018-10-01 Sample code and tools updated on github [code]
  • 2018-05-15 Kinship Classification through Latent Adaptive Subspace presented at 2018 FG in Xi'an, China [paper]
  • 2018-05-14 2018 Workshop on RFIW (version 2) at Automatic Face & Gesture (2018 FG)
  • 2018-03-14 Visual Recognition of Families In the Wild (FIW) published in TPAMI [    paper]
  • 2017-10-27 2017 RFIW Data Challenge Workshop at 2017 ACM MM in Mountain View, CA [proceedings, paper]
  • 2017-05-31 Verification FIW with Marginalized Denoising Metric Learning presented at 2017 FG [poster, paper]
  • 2017-05-30 Poster presented at 2017 FG in Washington DC [poster]
  • 2017-03-30 Built Project Page (this)
  • 2016-12-11 Poster presented at 2017 FG in Washington DC [poster]
  • 2016-05-30 Presented at 2016 New England Computer Vision Workshop at BU [extended abstract, slides]
  • 2016-10-15 FIW presented at ACM MM 2016 in Amsterdam, Netherlands [paper, poster]

Download Our Paper

Joseph P. Robinson, Ming Shao, Yue Wu, Hongfu Liu, Timothy Gillis, Yun Fu
IEEE Transactions on pattern analysis and machine intelligence (TPAMI)
Special Issue: Computational Face, 2018

Description

Families In the Wild (FIW) is the largest and most comprehensive image database for automatic kinship recognition.

Our motivation is to provide the resource needed for kinship recognition technologies to transition from research-to-reality. with over 11,932 family photos of 1,000 families FIW closely reflects the true data distribution of families worldwide (see Database for more information). There are many directions for FIW to take throughout the machine vision and related research communities (e.g., in relation to benchmarks (see Challenges and Results for details), new benchmarks, generative models, multi-modal learning…. to name a few). In terms of its practical value, many could benefit from FIW as well, such as the consumer (e.g. automatic photo library management), scholar (e.g. historic lineage & genealogical studies), analyzer (e.g. social-media-based analysis), investigator (e.g. missing persons and human traffickers).

Citation

	@inproceedings{fiwpamiSI2018,
	  Author = {Robinson, J P and Shao, M and Wu, Y and Liu, H and Gillis, T and Fu, Y},
	  Booktitle = {IEEE Transactions on pattern analysis and machine intelligence},
	  Title = {Visual Kinship Recognition of Families in the Wild},
          Keywords={Kin Verification, Classification, Semi-Supervised, Deep Learning},
	  Year = {2018}
          }
		

Contact

For questions and result submission, please contact Joseph Robinson at robinson.jo@husky.neu.edu