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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 472473, 5 pages
Research Article

Learning a Genetic Measure for Kinship Verification Using Facial Images

College of Information Engineering, Capital Normal University, Beijing 100048, China

Received 11 October 2014; Accepted 20 January 2015

Academic Editor: Yuri Vladimirovich Mikhlin

Copyright © 2015 Lu Kou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Motivated by the key observation that children generally resemble their parents more than other persons with respect to facial appearance, distance metric (similarity) learning has been the dominant choice for state-of-the-art kinship verification via facial images in the wild. Most existing learning-based approaches to kinship verification, however, are focused on learning a genetic similarity measure in a batch learning manner, leading to less scalability for practical applications with ever-growing amount of data. To address this, we propose a new kinship verification approach by learning a sparse similarity measure in an online fashion. Experimental results on the kinship datasets show that our approach is highly competitive to the state-of-the-art alternatives in terms of verification accuracy, yet it is superior in terms of scalability for practical applications.