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分辨性分解块稀疏表示遮挡人脸识别算法 被引量：4

Discriminative Decomposition Structure-Sparse Representation for Face Recognition with Occlusion
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摘要 针对遮挡人脸检测问题,将分辨性分解模型与块稀疏表示结合起来提出基于分辨性分解块稀疏表示的遮挡人脸识别算法。首先,利用该图像分解算法将训练图像集分解成共同部分、低秩条件部分和稀疏误差部分;其次,分别在共同部分和低秩条件部分上利用PCA构造投影矩阵,联合两个投影矩阵构造最终的投影矩阵,并对原训练集及测试样本进行投影;最后,在投影空间中利用块稀疏表示对测试样本进行分类识别。在AR数据库上的遮挡仿真实验证明,与SRC、NS、BS算法相比,该方法可以在低维特征空间上获得较高的识别率且具有更强的鲁棒性。 To solve the image recognition problem when existing occlusion,an algorithm combined Discriminative Decomposition （DD） model with structured sparse representation is proposed.First,images are decomposed to three parts,common component,low-rank condition component and sparse error component.Secondly,projection matrix on common component and low-rank component are computed respectively and the final projection matrix is obtained by fusing the two matrixes; Finally,the recognition step was constructed on the projection subspace using structured sparse representation.Experiment results on AR dataset prove our method perform better in recognition rate than BS （Block Sparse Representation）,NS （Nearest Subspace） and SRC in low-dimension.
作者 胡正平 赵淑欢
机构地区 燕山大学信息科学与工程学院
出处 《信号处理》 CSCD 北大核心 2014年第2期214-220,共7页 Journal of Signal Processing
基金 国家自然科学基金(No.61071199) 河北省自然科学基金(No.F2010001297)
关键词 低秩 分辨性分解 稀疏表示 块稀疏 Low-Rank Discriminative Decomposition Sparse Representation Block-Sparse
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献18

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共引文献116

同被引文献57

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信号处理
2014年 第2期

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