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 the benchmarked experiments (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).
Download Our Paper
Please cite the following paper if you Download and use any data or other resources of FIW: JP Robinson, M Shao, Y Wu, H Liu, T Gillis, Y Fu, Visual Kinship Recognition of Families In the Wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 [paper, appendix, slideshow, video] [BIBTEX]
If you have any questions, ideas, bug reports, or such, please do not hesitate to contact us!
News and Updates
2017
27 Oct. RFIW Data Challenge Workshop at ACM MM 2017 in Mountain View, CA [proceedings, paper, slides <coming soon>] 17 Oct. Recognizing Families In the Wild (RFIW) Data Challenge 2.0 at FG 2018 [webpage] 31 May Poster Presented at IEEE Automatic Face and Gesture in Washington DC [paper, poster] 30 May Poster Presented at IEEE Automatic Face and Gesture in Washington DC [poster] 15 May RFIW Codalab competition portals are open for teams and individuals to register [Track 1- Verification, Track 2- Classification] 10 Apr. Recognizing Families In the Wild (RFIW) Data Challenge at ACM MM 2017 [webpage] 15 Jan.Kinship Verification on Families in the Wild with Marginalized Denoising Metric Learning to be presented at FG 2017
2016
11 Dec. Built Project Page. 21 Nov. Presented at 2016 New England Computer Vision Workshop at BU [extended abstract, presentation (pdf,pptx)]. 15 Oct. FIW presented in Amsterdam, Netherlands at ACM MM 2016 [paper, poster].