| 融合全局和局部特征的稀疏表示人脸识别方法 |
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| 引用本文： | 刘亚亚,程国. 融合全局和局部特征的稀疏表示人脸识别方法[J]. 甘肃科学学报, 2017, 29(4). DOI: 10.16468/j.cnki.issn1004-0366.2017.04.004 |
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| 作者姓名： | 刘亚亚 程国 |
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| 作者单位： | 商洛学院 数学与计算机应用学院,陕西 商洛,726000 |
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| 基金项目： | 陕西省教育厅自然科学研究计划项目，商洛学院科研项目 |
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| 摘 要： | 针对人脸识别中如何提取到有效判别特征的问题,提出一种融合人脸图像全局和局部特征的稀疏表示人脸识别方法。首先将人脸图像用融合的特征提取算法进行特征降维,然后利用稀疏表示分类器对人脸图像进行分类判别。在ORL、Yale和FERET人脸数据库上的实验结果验证了融合算法在提高人脸识别精度方面是有效的。
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| 关 键 词： | 稀疏表示分类 最大间距准则 人脸识别 |
Method of Sparse Representation Face Recognition Fusing Global and Local Features |
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| Liu Yaya,Cheng Guo. Method of Sparse Representation Face Recognition Fusing Global and Local Features[J]. Journal of Gansu Sciences, 2017, 29(4). DOI: 10.16468/j.cnki.issn1004-0366.2017.04.004 |
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| Authors: | Liu Yaya Cheng Guo |
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| Abstract: | Aiming at the question of how to extract effective discriminant features in face recognition,a new method of sparse representation face recognition fusing global and local features of face images was proposed.Firstly,the fusion feature extraction algorithm was used to reduce the dimension of feature for the face images,and then the face images were classified and discriminated by sparse representation classifier.Experimental results on ORL,Yale and FERET face database showed that the fusion algorithm was effective in improving the accuracy of face recognition. |
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| Keywords: | Sparse representation classification Maximum margin criterion Face recognition |
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