Neighborhood Repulsed Metric Learning for Kinship Verification
Neighborhood Repulsed Metric Learning for Kinship Verification
复制标题
DOI：
10.1109/tpami.2013.134
复制
发表时间：
2014-02-01
影响因子：
23.6
通讯作者：
Zhou, Jie
中科院分区：
文献类型：
--
作者：
Lu, Jiwen;Zhou, Xiuzhuang;Zhou, Jie
从人脸图像中确认亲属关系是计算机视觉中一个有趣且具有挑战性的问题，文献中对解决这个问题的尝试非常有限。本文提出了一种新的邻域排斥度量学习（NRML）亲属关系验证方法。动机是这样一个事实，（没有亲属关系）具有较高相似性的样本通常位于一个邻域中，并且比具有较低相似性的样本更容易被误分类，我们的目标是学习一个距离度量，在该距离度量下，（具有亲属关系）被拉得尽可能近，而位于附近的类间样本被排斥并被推得尽可能远，同时，使得可以利用更多的区别性信息来进行验证。为了更好地利用多个特征描述符提取互补信息，本文进一步提出了一种多视图NRML（MNRML）方法，寻求一个共同的距离度量来进行多特征融合，以提高亲属关系验证的性能。实验结果表明，我们所提出的方法的有效性。最后，我们还测试了人类的能力，在亲属关系验证从面部图像和我们的实验结果表明，我们的方法是人类观察者相媲美。
Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle 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 a kinship relation) 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 a kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to extract complementary information, we further propose a multiview NRML (MNRML) method to seek a common distance metric to perform multiple feature fusion to improve the kinship verification performance. Experimental results are presented to demonstrate the efficacy of our proposed methods. Finally, we also test human ability in kinship verification from facial images and our experimental results show that our methods are comparable to that of human observers.