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 12,000 family photos of 1,001 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).
If you have any questions, ideas, specific requests, or such, please do not hesitate to contact us!
For details of data and labels Database. For description of task evaluations see Challenge. For benchmark results for each task see Results. For code see Github.
30 May Poster Presented at IEEE Automatic Face and Gesture in Washington DC [paper, 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
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].