The nearest-farthest subspace classification for face recognition
The nearest-farthest subspace classification for face recognition
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DOI：
10.1016/j.neucom.2013.01.003
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发表时间：
2013-08
期刊：
影响因子：
6
通讯作者：
Jian-Xun Mi;De-shuang Huang;Bing Wang;Xingjie Zhu
中科院分区：
文献类型：
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作者：
Jian-Xun Mi;De-shuang Huang;Bing Wang;Xingjie Zhu
最近子空间(NS)分类是利用线性回归技术解决人脸识别问题的一种有效方法。该方法基于这样的假设，即来自特定主题类的人脸图像往往跨越唯一的子空间，即特定于类的子空间。然后，测试图像与其自己的特定于类的子空间之间的距离最短。在本文中，我们提出了一种新的人脸识别思想。该思想认为，测试人脸图像应该远离除该测试图像类别中的图像之外的所有训练图像所跨越的最远的子空间。基于这一思想，我们提出了一种用于人脸识别的FS分类器。在我们看来，NS和FS分类器利用了类特定子空间的不同特征。NS分类器利用测试图像与单个类之间的关系，而FS分类器度量该测试图像与其余类之间的关系。因此，我们提出了最近-最远子空间(NFS)分类器，它利用这两种关系来对测试图像进行分类。在四个著名的公共人脸数据库上与NS分类器和其他最新方法进行了比较，结果表明FS和NFS具有良好的性能。
The nearest subspace (NS) classification is an efficient method to solve face recognition problem by using the linear regression technique. This method is based on the assumption that face images from a specific subject class tend to span a unique subspace, i.e. a class-specific subspace. Then, a test image has the shortest distance from its own class-specific subspace. In this paper, we present a novel idea for face recognition. This idea considers that a test face image should be far from the farthest subspace (FS) spanned by all training images except the images from the class of this test image. Based on this idea, we propose the FS classifier for face recognition. In our opinion, NS and FS classifiers take advantages of different characteristics of the class-specific subspace. NS classifier exploits the relationship between a test image and a single class while FS classifier measures relationship between this test image and the rest classes. Consequently, we propose the nearest-farthest subspace (NFS) classifier which exploits the both relationships to classify a test image. The comparisons with NS classifier and other state-of-the-art methods on four famous public face databases demonstrate the good performance of FS and NFS.