Two-dimensional margin, similarity and variation embedding
Two-dimensional margin, similarity and variation embedding
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DOI：
10.1016/j.neucom.2012.01.023
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发表时间：
2012-06
期刊：
影响因子：
6
通讯作者：
Quanxue Gao;Haijun Zhang;Jingjing Liu
中科院分区：
文献类型：
--
作者：
Quanxue Gao;Haijun Zhang;Jingjing Liu
已有的研究表明，基于流形的学习判别方法可以提高人脸识别的准确率。然而，他们忽略了来自同一类的相邻人脸图像之间的变化，这对于进一步提高识别精度和避免判别方法中的过拟合问题非常重要。为了避免这个问题，我们提出了一种新的人脸识别方法。在我们所提出的方法中，我们构建两个邻接图来分别对来自同一类的人脸图像的边缘和信息（包括相似性和变化）进行建模，然后将信息和边缘纳入降维函数中。实验证明了该方法的有效性。
Previous works have demonstrated that manifold-based learning discriminant approaches can improve the face recognition accuracy. However, they ignore the variation among nearby face images from the same class, which is important to further improve the recognition accuracy and avoid the over-fitting problem in discriminant approaches. To avoid this problem, we propose a novel approach for face recognition. In our proposed approach, we construct two adjacency graphs to model the margin and information including similarity and variation of face images from the same class, respectively, and then incorporate the information and margin into the dimensionality reduction function. Experiments demonstrate the effectiveness of our approach.