计算机与现代化

• 图像处理 • 上一篇    下一篇

基于鉴别性低秩表示的2阶段人脸识别算法

  

  1. (1.扬州大学广陵学院机械电子工程系,江苏扬州225000;2.扬州大学信息工程学院,江苏扬州225127)
  • 收稿日期:2019-08-26 出版日期:2019-12-11 发布日期:2019-12-11
  • 作者简介:崔娟娟(1989-),女,江苏东台人,讲师,硕士,研究方向:模式识别,E-mail: 873950185@qq.com; 通信作者:张蕾(1991-),女,硕士研究生,研究方向:数据挖掘,E-mail: zhangleiyzu@163.com; 侯谢炼(1996-),女,硕士研究生,研究方向:模式识别,人工智能,E-mail: xielhou@163.com; 陈才扣(1967-),男,教授,博士,研究方向:模式识别,人工智能,E-mail: yzcck@126.com; 张海燕(1978-),女,讲师,博士,研究方向:电子电路。
  • 基金资助:
    扬州大学广陵学院自然科学重点研究项目(ZKZD19001, ZKZD18002)

A Two-phase Face Recognition Algorithm Based on Discriminative Low-rank Representation

  1. (1. Department of Mechanical and Electronic Engineering, Guangling College, Yangzhou University, Yangzhou 225000, China;
    2. School of Information Engineering, Yangzhou University, Yangzhou 225127, China)
  • Received:2019-08-26 Online:2019-12-11 Published:2019-12-11

摘要: 针对图像训练样本中存在噪声等情况,提出一种基于鉴别性低秩表示的2阶段人脸识别算法。该算法第1阶段是对所有训练样本进行低秩处理,筛选出M类与测试样本最相近的样本用于粗分类;第2阶段使用第1阶段筛选出来的样本做鉴别性低秩表示处理,并使用稀疏线性表示进行精细分类,决定测试样本最适合的类标签。本算法结合了低秩算法与稀疏算法的优点,在标准人脸库上的实验表明本算法表现优越。

关键词: 机器视觉, 人脸识别, 低秩表示, 变换算法

Abstract:  A two-phase face recognition algorithm based on discriminative low-rank representation is proposed to deal with the noise in image training samples. In the first stage, all the training samples are processed by low-rank representation, and the M nearest neighbors of test sample are selected for rough classification. In the second stage, the samples screened in the first stage are used for discriminative low-rank representation, and sparse linear representation is used for fine classification, so as to determine the most suitable class labels for test samples. This algorithm combines the advantages of low-rank algorithm and sparse algorithm. The performance of this algorithm is proved by experiments on standard face database.

Key words:  machine vision, face recognition, low-rank representation, transformation algorithm

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