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|Title:||Neighborhood repulsed metric learning for kinship verification||Authors:||Lu, Jiwen
Tan, Yap Peng
|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
|DOI:||10.1109/CVPR.2012.6247978||Rights:||© 2012 IEEE.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
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