[1]吴小艺,吴小俊.结构化加权稀疏低秩恢复算法在人脸识别中的应用[J].智能系统学报,2019,14(3):455-463.[doi:10.11992/tis.201711026]
WU Xiaoyi,WU Xiaojun.A low rank recovery algorithm for face recognition with structured and weighted sparse constraint[J].CAAI Transactions on Intelligent Systems,2019,14(3):455-463.[doi:10.11992/tis.201711026]
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WU Xiaoyi,WU Xiaojun.A low rank recovery algorithm for face recognition with structured and weighted sparse constraint[J].CAAI Transactions on Intelligent Systems,2019,14(3):455-463.[doi:10.11992/tis.201711026]
结构化加权稀疏低秩恢复算法在人脸识别中的应用
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第3期
页码:
455-463
栏目:
学术论文—机器感知与模式识别
出版日期:
2019-05-05
- Title:
- A low rank recovery algorithm for face recognition with structured and weighted sparse constraint
- Keywords:
- face recognition; structured; weighted sparse; low-rank representation; subspace projection
- 分类号:
- TP391
- 摘要:
- 针对训练样本或测试样本存在污损的情况，提出一种结构化加权稀疏低秩恢复算法（structured and weighted-sparse low rank representation，SWLRR）。SWLRR对低秩表示进行加权稀疏约束和结构化约束，使得低秩表示系数更加趋近于块对角结构，进而可获得具有判别性的低秩表示。SWLRR将训练样本恢复成干净训练样本后，再根据原始训练样本和恢复后的训练样本学习到低秩投影矩阵，把测试样本投影到相应的低秩子空间，即可有效地去除测试样本中的污损部分。在几个人脸数据库上的实验结果验证了SWLRR在不同情况下的有效性和鲁棒性。
- Abstract:
- Herein, a structured and weighted sparse low-rank recovery algorithm (SWLRR) is proposed to deal with trained or tested samples that are corrupt. The SWLRR constrains the low-rank representation by incorporating the structured and weighted sparse constraints, enabling the low-rank representation coefficient matrix to be closer to the block diagonal. Then, a discriminative structured representation can be obtained. After recovering the clean training samples from the corrupted training samples using SWLRR, the low-rank projection matrix is learnt by the low-rank projection matrix according to the original and recovered training samples, whereas the test samples are projected into the corresponding low-rank subspaces. In this way, the corrupted regions can be removed efficiently from the test samples. The experimental results on several face databases validate the effectiveness and robustness of the SWLRR under different situations.
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备注/Memo
收稿日期:2017-11-21。
基金项目:国家自然科学基金项目（61672265，61373055）；江苏省教育厅科技成果产业化推进项目（JH10-28）；江苏省产学研创新项目（BY2012059）.
作者简介:吴小艺,女,1994年生,硕士研究生,主要研究方向为人脸识别、稀疏低秩表示、字典学习;吴小俊,男,1967年生,教授,博士生导师,主要研究方向为人工智能、模式识别、计算机视觉。研究成果获得省部级以上奖励5项。完成包括国防973子课题、IEEE智慧城市国际合作项目、国家自然科学基金项目和教育部重大科研课题研究项目。发表学术论文200余篇,被SCI检索50余篇、EI检索100余篇,出版学术著作5部。
通讯作者:吴小俊.E-mail:xiaojun_wu_jnu@163.com
更新日期/Last Update:
1900-01-01