Families In the Wild: A Kinship Recognition Benchmark
If you found our data and resources useful please cite our works.
2018
2017
Joseph P. Robinson, Ming Shao, Handong Zhao, Yue Wu, Timothy Gillis, Yun Fu
Recognizing Families In the Wild (RFIW): Data Challenge Workshop in Conjunction with ACM MM 2017
Proceedings of the Workshop on Recognizing Families In the Wild, 2017
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@Article{Robinson:2017:RFW:3134421.3134424,
author = "Robinson, Joseph P. and Shao, Ming and Zhao, Handong and Wu, Yue and
Gillis, Timothy and Fu, Yun",
title = "Recognizing Families In the Wild (RFIW): Data Challenge Workshop in
Conjunction with ACM MM 2017",
booktitle = "Proceedings of the 2017 Workshop on Recognizing Families In the Wild",
pages = "5--12",
numpages = "8",
isbn = "978-1-4503-5511-7",
series = "RFIW '17",
year = "2017",
location = "Mountain View, California, USA",
url = "http://doi.acm.org/10.1145/3134421.3134424",
doi = "10.1145/3134421.3134424",
acmid = "3134424",
publisher = "ACM",
address = "New York, NY, USA",
keywords = "acm mm workshop, algorithmic design, big data, convolutional neural networks,
data challenge, deep learning, evaluation, facial recognition, family
classification, kinship verification, large image database, large-scale
kinship recognition, metric learning, visual understanding"
}
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.
2016
Joseph P. Robinson, Ming Shao, Yue Wu, Yun Fu
Families in the Wild (FIW): Large-scale Kinship Image Database and Benchmarks
ACM on Multimedia Conference, 2016
@InProceedings{robinson2016families,
author = "Robinson, Joseph P. and Shao, Ming and Wu, Yue and Fu, Yun",
title = "Families In the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks",
booktitle = "Proceedings of the 2016 ACM on Multimedia Conference",
pages = "242--246",
publisher = "ACM",
year = "2016"
}
We present the largest kinship recognition dataset to date, Families in the Wild (FIW). Motivated by the lack
of a single, unified dataset for kinship recognition, we aim to provide a dataset that captivates the
interest of the research community. With only a small team, we were able to collect, organize, and label over 10,000 family photos of 1,000 families with our annotation tool designed to mark complex hierarchical
relationships and local label information in a quick and efficient manner. We include several benchmarks for two image-based tasks, kinship verification and family recognition. For this, we incorporate several
visual features and metric learning methods as baselines. Also, we demonstrate that a pre-trained Convolutional Neural Network (CNN) as an off-the-shelf feature extractor outperforms the other fea- ture types. Then, results were further boosted by fine-tuning two deep CNNs on FIW data: (1) for kinship verification, a triplet loss function was learned on top of the network of pre-train weights; (2) for family recognition, a family-specific softmax classifier was added to the network.
2012
2011
Siyu Xia, Ming Shao, Yun Fu
Kinship Verification through Transfer Learning
International Joint Conference on Artificial Intelligence (IJCAI), 2011
@InProceedings{xia2011kinship,
author = "Xia, Siyu and Shao, Ming and Fu, Yun",
title = "Kinship verification through transfer learning",
journal = "IJCAI",
year = "2011",
pages = "2539--2544"
}
Because of the inevitable impact factors such as pose, expression,
lighting and aging on faces, identity verification through faces is
still an unsolved problem. Research on biometrics raises an even
challenging problem–is it possible to determine the kinship merely
based on face images? A critical observation that faces of parents
captured while they were young are more alike their children’s compared
with images captured when they are old has been revealed by genetics
studies. This enlightens us the following research. First, a new kinship
database named UB KinFace composed of child, young parent and old parent
face images is collected from Internet. Second, an extended transfer
subspace learning method is proposed aiming at mitigating the enormous
divergence of distributions between children and old parents. The key
idea is to utilize an intermediate distribution close to both the source
and target distributions to bridge them and reduce the divergence.
Naturally the young parent set is suitable for this task. Through this
learning process, the large gap between distributions can be
significantly reduced and kinship verification problem becomesmore
discriminative. Experimental results show that our hypothesis on the
role of young parents is valid and transfer learning is effective to
enhance the verification accuracy.
