@inproceedings{d9ce60ca1d0048629d9d8251996b8c43,
title = "Kinship Verification via Reference List Comparison",
abstract = "Kinship verification based on facial images has attracted the attention of pattern recognition and computer vision community. Most of existing methods belong to supervised mode, in which they need to know the labels of training samples. In this paper, we adapt an unsupervised method via Reference List Comparison (RLC) for kinship verification task, which does not use external data or data augmentation. Specifically, we obtain a reference list by calculating the similarities of a probe image and all the images in the reference set. Given two probe face images, their similarity is reflected by the similarity of the two ordered reference lists. Experimental results on the KinFaceW-I and KinFaceW-II datasets show the effectiveness of RLC approach for kinship verification.",
keywords = "Kinship verification, Similarity, Unsupervised learning",
author = "Wenna Zheng and Junlin Hu",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 15th Chinese Conference on Biometric Recognition, CCBR 2021 ; Conference date: 10-09-2021 Through 12-09-2021",
year = "2021",
doi = "10.1007/978-3-030-86608-2\_42",
language = "英语",
isbn = "9783030866075",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "384--391",
editor = "Jianjiang Feng and Junping Zhang and Manhua Liu and Yuchun Fang",
booktitle = "Biometric Recognition - 15th Chinese Conference, CCBR 2021, Proceedings",
address = "德国",
}