面部识别系统
线性子空间
模式识别(心理学)
特征提取
人工智能
子空间拓扑
计算机科学
矩阵范数
离群值
稳健性(进化)
增广拉格朗日法
秩(图论)
面子(社会学概念)
低秩近似
计算机视觉
数学
算法
特征向量
张量(固有定义)
几何学
量子力学
基因
组合数学
物理
社会学
生物化学
社会科学
化学
纯数学
作者
Ming Yin,Shuting Cai,Junbin Gao
标识
DOI:10.1109/icip.2013.6738777
摘要
Feature extraction is one of the most fundamental problems in face recognition tasks. In this paper, motivated by low-rank representation (LRR) model on exploring the multiple subspace structures of observation data, we propose a double low-rank matrix recovery method to learn low-rank subspaces from face images, where it takes into account the recovery of row space and column space information simultaneously. Applying Augmented Lagrangian Multiplier (ALM), the optimization problem on minimization of nuclear norm is resolved efficiently. By evaluating on public face databases, experimental results show that our proposed method works much better than existing face recognition methods based on feature extraction. It is more robust to outliers, varying illumination and occlusion.
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