Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98388
Title: Neighborhood repulsed metric learning for kinship verification
Authors: Lu, Jiwen
Hu, Junlin
Zhou, Xiuzhuang
Shang, Yuanyuan
Tan, Yap Peng
Wang, Gang
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Lu, J., Hu, J., Zhou, X., Shang, Y., Tan, Y. P., Wang, G., et al. (2012). Neighborhood repulsed metric learning for kinship verification. 2012 IEEE Conference on Computer Vision and Pattern Recognition.
Abstract: Kinship verification from facial images is a challenging problem in computer vision, and there is a very few attempts on tackling this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without kinship relations) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with kinship relations) are pushed as close as possible and interclass samples lying in a neighborhood are repulsed and pulled as far as possible, simultaneously, such that more discriminative information can be exploited for verification. Moreover, we propose a multiview NRM-L (MNRML) method to seek a common distance metric to make better use of multiple feature descriptors to further improve the verification performance. Experimental results are presented to demonstrate the efficacy of the proposed methods.
URI: https://hdl.handle.net/10356/98388
http://hdl.handle.net/10220/12489
DOI: 10.1109/CVPR.2012.6247978
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.