Challenge report: Recognizing Families In the Wild Data Challenge
- URL: http://arxiv.org/abs/2006.00154v1
- Date: Sat, 30 May 2020 03:01:56 GMT
- Title: Challenge report: Recognizing Families In the Wild Data Challenge
- Authors: Zhipeng Luo, Zhiguang Zhang, Zhenyu Xu, Lixuan Che
- Abstract summary: This paper is a brief report to our submission to the Recognizing Families In the Wild Data Challenge (4th Edition), in conjunction with FG 2020 Forum.
In this paper, we studied previous methods and proposed our method. We try many methods, like deep metric learning-based, to extract deep embedding feature for every image, then determine if they are blood relatives by Euclidean distance or method based on classes.
- Score: 17.070016479682096
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper is a brief report to our submission to the Recognizing Families In
the Wild Data Challenge (4th Edition), in conjunction with FG 2020 Forum.
Automatic kinship recognition has attracted many researchers' attention for its
full application, but it is still a very challenging task because of the
limited information that can be used to determine whether a pair of faces are
blood relatives or not. In this paper, we studied previous methods and proposed
our method. We try many methods, like deep metric learning-based, to extract
deep embedding feature for every image, then determine if they are blood
relatives by Euclidean distance or method based on classes. Finally, we find
some tricks like sampling more negative samples and high resolution that can
help get better performance. Moreover, we proposed a symmetric network with a
binary classification based method to get our best score in all tasks.
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