Face recognition algorithm based on discriminative dictionary learning and sparse representation

Face recognition algorithm based on discriminative dictionary learning and sparse representation
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基于判别字典学习和稀疏表示的人脸识别算法

DOI:
10.1016/j.neucom.2015.09.091
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发表时间:
2016-01
期刊:
影响因子:
6
通讯作者:
Zhang Linghua
Zhang Linghua
中科院分区:
计算机科学2区
文献类型:
--
作者:
Lu Zhenyu;Zhang Linghua

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为了克服现有的光照、遮挡、姿势变化等非受控环境下人脸识别率大大降低的缺陷,提出了基于判别字典学习和正则化鲁棒编码的人脸识别算法。在该算法中,首先使用Gabor滤波器获得人脸图像的Gabor幅度图像,然后提取均匀局部二值直方图并使用Fisher准则获得新的字典,最后通过稀疏表示编码将测试图像分类为现有类别。 Extended Yale B数据库和AR数据库的实验结果表明,与K-SVD、LC-K-SVD、FDDL等相比,该算法在现有的非受控环境下具有更高的人脸识别率。
In order to overcome the defect that the face recognition (FR) rate is greatly reduced in the existing uncontrolled environments such as the change of illumination, occlusion, and posture, etc, Face recognition algorithm based on discriminative dictionary learning and regularized robust coding was proposed. In this proposed algorithm, the Gabor amplitude images of a face image are obtained via using Gabor filter at first, then we extract the uniform local binary histogram and use Fisher criterion to gain a new dictionary, finally the test image is classified as the existing class via sparse representation Coding. The experimental results obtained from Extended Yale B databases and AR databases show that the proposed algorithm has higher face recognition rate in the existing uncontrolled environments in comparison with K-SVD, LC-K-SVD, FDDL and so on.
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