Neighborhood repulsed metric learning for kinship verification

• Main Authors: Lu, Jiwen, Hu, Junlin, Zhou, Xiuzhuang, Shang, Yuanyuan, Tan, Yap Peng, Wang, Gang
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/98388; http://hdl.handle.net/10220/12489
• Institution: Nanyang Technological University

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.