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山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (1): 15-20.doi: 10.6040/j.issn.1672-3961.0.2017.005

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基于低秩表示投影的无监督人脸特征提取

张振月,李斐,江铭炎 *   

  1. 山东大学信息科学与工程学院, 山东 济南 250100
  • 收稿日期:2017-01-03 出版日期:2018-02-20 发布日期:2017-01-03
  • 通讯作者: 江铭炎(1964— ),男,江苏苏州人,教授,博士,主要研究方向为计算机视觉,机器学习,智能优化.E-mail:jiangmingyan@sdu.edu.cn E-mail:zhangzhenyue0@163.com
  • 作者简介:张振月(1989— ),男,山东菏泽人,硕士研究生,主要研究方向为低秩表示,人脸识别,机器学习.E-mail:zhangzhenyue0@163.com
  • 基金资助:
    国家自然科学基金资助项目(61201370);山东省自然科学基金资助项目(ZR2014FM039)

Unsupervised face image feature extraction based on low-rank representation projection

ZHANG Zhenyue, LI Fei, JIANG Mingyan*   

  1. School of Information Science and Engineering, Shandong University, Jinan 250100, Shandong, China
  • Received:2017-01-03 Online:2018-02-20 Published:2017-01-03

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摘要： 为了构造数据之间的自适应邻接图,同时克服稀疏表示系数和协同表示系数互相独立、提取全局信息弱的缺陷,提出采用低秩表示(low-rank representation, LRR)系数构造权重矩阵的流形学习算法,即低秩表示投影(low-rank representation projections, LRRP)和判别低秩表示投影(discriminative low-rank representation projections, DLRRP)。在新算法中,将低秩表示系数表征的样本之间的邻接关系保留在特征空间;同时利用低秩系数的聚类性质,在优化目标中加入类内散度最小化项,计算出具有判别性的投影矩阵。试验结果表明,在真实人脸图像库上与其他几种流形学习算法相比,LRRP和DLRRP能够取得更好的识别率。提出的新算法是有效的特征提取算法,能够丰富流形学习框架。

关键词: 人脸识别, 低秩表示, 流形学习, 邻接图, 特征提取

Abstract: In order to construct the adaptive adjacency graph between data points, and also to overcome the disadvantage that the coefficients of sparse representation and collaborative representation were independent, the low-rank representation projections(LRRP)and discriminative low-rank representation projections(DLRRP)were proposed. In these two manifold learning methods, the weighted matrix was constructed by low-rank representation(LRR). The adjacencies defined by the coefficients were preserved in the feature space. By virtue of the clustering property of the coefficients, an within-class scatter minimum term was added in the optimization objective, which leaded to a discriminative projection. The experimental results showed that compared with other manifold learning algorithms, LRRP and DLRRP could obtain the better recognition accuracies. The proposed methods were effective feature extraction algorithms and enriched the manifold learning framework.

Key words: manifold learning, low-rank representation, face recognition, feature extraction, adjacency graph

中图分类号: 

  • TP391
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