Using the idea of the sparse representation to perform coarse-to-fine face recognition
Using the idea of the sparse representation to perform coarse-to-fine face recognition
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DOI：
10.1016/j.ins.2013.02.051
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Yong Xu;Qiong-xia Zhu;Zizhu Fan;D. Zhang;Jian-Xun Mi;Zhihui Lai
中科院分区：
文献类型：
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作者：
Yong Xu;Qiong-xia Zhu;Zizhu Fan;D. Zhang;Jian-Xun Mi;Zhihui Lai
本文提出了一种由粗到精的人脸识别方法。该方法由两个阶段组成，其工作方式与众所周知的稀疏表示方法类似。第一阶段确定近似等于测试样本的所有训练样本的线性组合。该阶段利用所确定的线性组合来粗略地确定测试样本的候选类别标签。第二阶段再次确定候选类别中近似等于测试样本的所有训练样本的加权和，并使用该加权和来执行分类。该方法的基本原理如下：第一阶段识别出距离测试样本“很远”的类，并将它们从训练样本集中删除。然后，该方法将测试样本分配到剩余的一个类中，并且分类问题变得更简单，具有更少的类。该方法不仅具有较高的精度，而且解释清晰。
In this paper, we propose a coarse-to-fine face recognition method. This method consists of two stages and works in a similar way as the well-known sparse representation method. The first stage determines a linear combination of all the training samples that is approximately equal to the test sample. This stage exploits the determined linear combination to coarsely determine candidate class labels of the test sample. The second stage again determines a weighted sum of all the training samples from the candidate classes that is approximately equal to the test sample and uses the weighted sum to perform classification. The rationale of the proposed method is as follows: the first stage identifies the classes that are “far” from the test sample and removes them from the set of the training samples. Then the method will assign the test sample into one of the remaining classes and the classification problem becomes a simpler one with fewer classes. The proposed method not only has a high accuracy but also can be clearly interpreted.