mirror of FIW-MM dataset - a large-scale kinship recognition dataset
THIS IS WORK IN PROGRESS. Our goal is to provide reproducible code for most all the steps in the building, preprocessing, and experimenting on FIW-MM [1].
FIW-MM contains over X utterances for Y subjects, extracted from videos uploaded to YouTube.
The dataset is gender balanced (?), with ??% of the speakers male.
The speakers span a wide range of different ethnic groups, accents, professions and ages.
There are no overlapping families between development and test sets.
| train | test | |
|---|---|---|
| # of families | -- | - |
| # of speakers | ||
| # of videos | ||
| # of utterances |
Nationality Distribution: The nationalities of the speakers in the dataset were obtained by crawling Wikipedia and can be found (@zaid, correct) here.
You can also view the distribution in the following graph:
@TODO
.. image:: ./data/v1/distribution.png
The train/val/test split used in [1] below for kinship recognition can be found here.
Models:
- Pretrained models from dataset authors for VGGFIW - Kinship Identification and Verification [1] can be found here.
Notice:
We are preparing an extended dataset (FIW-MM-2), containing up to double the number of families and many more speakers and videos.
FIW was originally released in 2016 as an image-based DB [2]. Then, in 2018, FIW was extended by great amounts of data and label fixes [3].
Publications:
[1] Joseph P. Robinson, Zaid Khan, Ming Shao, Yun Fu - Families In Wild Multimedia (FIW-MM): A Multi-Modal Database for Recognizing Kinship - ACM on Multimedia Conference, 2020.
[2] Joseph P. Robinson, Ming Shao, Yue Wu, Hongfu Liu, Timothy Gillis, Yun Fu - Visual Kinship Recognition of Families in the Wild - IEEE Transactions on pattern analysis and machine intelligence (TPAMI), 2018.
[3] 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.
Several other works of ours are listed in the publication section of the FIW website [link].