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胡静,陶洋.基于RPCA的群稀疏表示人脸识别方法[J].重庆邮电大学学报(自然科学版),2020,32(3):459-468. 本文二维码信息
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基于RPCA的群稀疏表示人脸识别方法
Group sparse representation face recognition method based on RPCA
投稿时间:2018-10-26  修订日期:2020-05-22
DOI: 10.3979/j.issn.1673-825X.2020.03.016
中文关键词:  人脸识别  鲁棒主成分分析  低秩映射矩阵  群稀疏
English Keywords:face recognition  robust principal component analysis  low rank mapping matrix  group sparse
基金项目:国家自然科学基金(61801072);重庆市自然科学基金(cstc2018jcyjAX0344)
作者单位E-mail
胡静 重庆邮电大学 通信与信息工程学院,重庆 400065 113511986@qq.com 
陶洋 重庆邮电大学 通信与信息工程学院,重庆 400065 taoyang@cqupt.edu.cn 
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中文摘要:
      针对训练样本图像和测试样本图像均存在光照、污染、遮挡等情况下的人脸识别问题,提出一种基于鲁棒主成分分析的群稀疏表示人脸识别方法(group sparse representation face recognition method based on robust principal component analysis, GSR-RPCA)。该方法将人脸图像由空域变换到对数域,增强人脸图像的对比度,并通过结构非相关鲁棒主成分分析算法从训练样本图像矩阵D中分解出干净的低秩部分人脸图像矩阵A和误差图像矩阵E,以增强恢复数据的鉴别力;学习A与D之间的低秩映射关系矩阵P,并用P将存在遮挡的测试样本映射到其潜在的子空间下,得到干净的测试样本y;计算y在A上的群稀疏表示系数,并利用类关联重构残差对测试人脸进行识别,获得测试人脸的所属类别。在CMU PIE,Extended Yale B和AR数据库上的实验结果显示,提出方法具有较高的识别率和较强的鲁棒性。
English Summary:
      Due to the face recognition problem of illumination, pollution and occlusion in the test images and training images, a study of group sparse representation face recognition method based on robust principal component analysis with(GSR-RPCA)is proposed. Firstly,transferring the face image from the spatial domain to the logarithmic domain to enhance the contrast of the face image, and decomposes the clean low-rank partial face image from the training sample image matrix D by the structural incoherence robust principal component analysis algorithm. The matrix A and the error image matrix E are used to enhance the discriminating ability of the recovered data. Then, the low rank mapping relationship matrix P between A and D is learned, and P is used to map the occlusion test samples to their potential subspaces to get a clean test sample y. Finally, the group sparse representation coefficient of y is calculated, and the test face is identified by using the class association reconstruction residual to obtain the category of the test face. Experimental results on CMU PIE, Extended Yale B and AR database verify the effectiveness and robustness of our method.
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