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首页> 外文期刊>International Journal of Innovative Computing Information and Control >AN IMPROVEMENT TO THE NEAREST NEIGHBOR CLASSIFIER AND FACE RECOGNITION EXPERIMENTS
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AN IMPROVEMENT TO THE NEAREST NEIGHBOR CLASSIFIER AND FACE RECOGNITION EXPERIMENTS

机译：最近邻分类器和人脸识别实验的改进

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摘要

The conventional nearest neighbor classifier (NNC) directly exploits the distances between the test sample and training samples to perform classification. NNC independently evaluates the distance between the test sample and a training sample. In this paper, we propose to use the classification procedure of sparse representation to improve NNC. The proposed method has the following basic idea: the training samples are not uncorrelated and the "distance" between the test sample and a training sample should not be independently calculated and should take into account the relationship between different training samples. The proposed method first uses a linear combination of all the training samples to represent the test sample and then exploits modified "distance" to classify the test sample. The method obtains the coefficients of the linear combination by solving a linear system. The method then calculates the distance between the test sample and the result of multiplying each training sample by the corresponding coefficient and assumes that the test sample is from the same class as the training sample that has the minimum distance. The method elaborately modifies NNC and considers the relationship between different training samples, so it is able to produce a higher classification accuracy. A large number of face recognition experiments on three face image databases show that the maximum difference between the accuracies of the proposed method and NNC is greater than 10%.
机译：常规最近邻分类器（NNC）直接利用测试样本和训练样本之间的距离进行分类。 NNC独立评估测试样本和训练样本之间的距离。在本文中，我们建议使用稀疏表示的分类程序来改进NNC。所提出的方法具有以下基本思想：训练样本不是不相关的，并且不应独立地计算测试样本与训练样本之间的“距离”，并且应考虑不同训练样本之间的关系。提出的方法首先使用所有训练样本的线性组合来表示测试样本，然后利用修改的“距离”对测试样本进行分类。该方法通过求解线性系统来获得线性组合的系数。然后，该方法计算测试样本与每个训练样本乘以相应系数的结果之间的距离，并假定测试样本与具有最小距离的训练样本属于同一类别。该方法对NNC进行了精心修改，并考虑了不同训练样本之间的关系，因此能够产生较高的分类精度。在三个人脸图像数据库上进行的大量人脸识别实验表明，所提方法与NNC的精度之间的最大差异大于10％。

著录项

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  • 作者

    Yong Xu; Qi Zhu; Yan Chen; Jeng-Shyang Pan;

  • 作者单位

    Bio-Computing Research Center Harbin Institute of Technology Shenzhen Graduate School HIT Campus of Shenzhen University Town, Xili, Shenzhen 518055, P. R. China;

    Bio-Computing Research Center Harbin Institute of Technology Shenzhen Graduate School HIT Campus of Shenzhen University Town, Xili, Shenzhen 518055, P. R. China;

    Bio-Computing Research Center Harbin Institute of Technology Shenzhen Graduate School HIT Campus of Shenzhen University Town, Xili, Shenzhen 518055, P. R. China;

    Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School HIT Campus of Shenzhen University Town, Xili, Shenzhen 518055, P. R. China;

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  • 正文语种 eng
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  • 关键词

    face recognition; nearest neighbor classifier; sparse representation; classification;

    机译：人脸识别;最近邻居分类器;稀疏表示分类;

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