欢迎访问《图学学报》 分享到:

图学学报

• 视觉与图像 • 上一篇    下一篇

增量式鉴别非负矩阵分解算法及其在人脸识别中的应用

  

  1. 1. 浙江工业大学计算机科学与技术学院,浙江 杭州 310023;
    2. 浙江警察学院刑事科学技术系,浙江 杭州 310053;
    3. 基于大数据架构的公安信息化应用公安部重点实验室,浙江 杭州 310053
  • 出版日期:2017-10-31 发布日期:2017-11-03
  • 基金资助:
    国家自然科学基金项目(61379123);浙江省自然科学基金项目(LY12F02018);浙江省教育厅科研资助项目(Y201431023)

Incremental Discriminant Non-Negative Matrix Factorization and Its Application to Face Recognition

  1. 1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou Zhejiang 310023, China;
    2. Department of Forensic Science, Zhejiang Police College, Hangzhou Zhejiang, 310053 China;
    3. Key Laboratory of Police Information Application Based on Big Data Architecture Ministry of Public Security, Hangzhou Zhejiang 310053, China
  • Online:2017-10-31 Published:2017-11-03

摘要: 针对在线学习的人脸识别效率问题,提出了一种增量式鉴别非负矩阵分解算法。
在以往无监督学习模式下的增量式非负矩阵分解算法基础上,利用初始训练样本数据和新增训
练样本的类别信息,将同类别训练样本对应的系数向量均值作为初始迭代值,并在类内欧氏距
离最小的约束下建立目标泛函,从而获得更具鉴别性的特征并使优化求解时所需迭代次数明显
减少。通过在 ORL 和 PIE 人脸数据库上的实验验证了该算法收敛速度快、分类精度高,且较批
量式算法有更高的效率优势。

关键词: 人脸识别, 有监督学习, 非负矩阵分解, 增量学习

Abstract: Aiming at the efficiency and efficacy of online feature learning for face recognition, a novel
incremental discriminant learning algorithm of non-negative matrix factorization is proposed. Compared
with former unsupervised online matrix factorization methods, our approach takes good advantage of the
label information of training samples. Coefficient vector means of the same class were used for
initialization, then the algorithm iterates with constraints on minimizing Euclidean distance of in-class
samples. Which results more discriminative features and less iterations in computation. Experiments on
ORL and PIE face datasets demonstrated that the proposed algorithm achieved better classification
accuracy and converged faster than former batch based non-negative matrix factorization algorithms.

Key words: face recognition, supervised learning, non-negative matrix factorization, incremental learning