Recognizing Families In the Wild (RFIW): Data Challenge Workshop in Conjunction with ACM MM 2017

Submitted by grigby1 on Tue, 02/06/2018 - 1:44pm

Title	Recognizing Families In the Wild (RFIW): Data Challenge Workshop in Conjunction with ACM MM 2017
Publication Type	Conference Paper
Year of Publication	2017
Authors	Robinson, Joseph P., Shao, Ming, Zhao, Handong, Wu, Yue, Gillis, Timothy, Fu, Yun
Conference Name	Proceedings of the 2017 Workshop on Recognizing Families In the Wild
Date Published	October 2017
Publisher	ACM
Conference Location	New York, NY, USA
ISBN Number	978-1-4503-5511-7
Keywords	acm mm workshop, algorithmic design, Big Data, big data security metrics, convolutional neural networks, data challenge, Deep Learning, evaluation, facial recognition, family classification, kinship verification, large image database, large-scale kinship recognition, metric learning, Metrics, pubcrawl, Resiliency, Scalability, visual understanding
Abstract	Recognizing Families In the Wild (RFIW) is a large-scale, multi-track automatic kinship recognition evaluation, supporting both kinship verification and family classification on scales much larger than ever before. It was organized as a Data Challenge Workshop hosted in conjunction with ACM Multimedia 2017. This was achieved with the largest image collection that supports kin-based vision tasks. In the end, we use this manuscript to summarize evaluation protocols, progress made and some technical background and performance ratings of the algorithms used, and a discussion on promising directions for both research and engineers to be taken next in this line of work.
URL	https://dl.acm.org/doi/10.1145/3134421.3134424
DOI	10.1145/3134421.3134424
Citation Key	robinson_recognizing_2017

Groups:

Science of Security VO