.png){kind=icon}
返回
Matrix regression-based classification with block-norm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2019.07.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we consider the robust face recognition problem via matrix regression-based classification with block-norm(MRC-block). Specifically, we propose an inner-class sparse representation classification approach in which images are expressed as matrices instead of vectors and adopted to encode more discriminative information than other regression-based methods. In the regression step, a block-norm based matrix regression model is proposed, which can efficiently depress the effect of occlusion in probe images. Accordingly, an efficient algorithm is derived to optimize the proposed objective function. Comprehensive experiments on representative datasets demonstrate that MRC-block is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face image with occlusion. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Face recognition
Block-norm
Regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
The FERET database and evaluation procedure for face-recognition algorithms人脸识别算法的FERET数据库和评估程序
PREAI
分享收藏分享收藏Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median检测异常值: 不使用平均值周围的标准偏差，使用中位数周围的绝对偏差
OAAI
OAAI分享收藏Face recognition by sparse discriminant analysis via joint L2,1-norm minimization通过联合L2，1-范数最小化的稀疏判别分析进行人脸识别.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容