计算机应用 ›› 2011, Vol. 31 ›› Issue (10): 2728-2730.DOI: 10.3724/SP.J.1087.2011.02728

• 图形图像技术 • 上一篇    下一篇

基于样本扩充和改进2DPCA的单样本人脸识别

赵雅英,谭延琪,马小虎   

  1. 苏州大学 计算机科学与技术学院,江苏 苏州 215006
  • 收稿日期:2011-04-06 修回日期:2011-06-08 发布日期:2011-10-11 出版日期:2011-10-01
  • 通讯作者: 马小虎
  • 作者简介:赵雅英(1986-),女,江苏太仓人,硕士研究生,主要研究方向:数字图像处理,模式识别;谭延琪(1988-),男,湖南衡阳人,硕士研究生,主要研究方向:数字图像处理、模式识别;马小虎(1964-),男,江苏苏州人,教授,博士,主要研究方向:计算机图形学、模式识别、中文信息处理。
  • 基金资助:

    苏州市科技计划项目(SG201005)

Single sample face recognition based on sample augment and improved 2DPCA

ZHAO Ya-ying, TAN Yan-qi, MA Xiao-hu   

  1. School of Computer Science and Technology, Soochow University, Suzhou Jiangsu 215006, China
  • Received:2011-04-06 Revised:2011-06-08 Online:2011-10-11 Published:2011-10-01
  • Contact: Ma XiaoHu

摘要: 针对大多数人脸识别方法在单个训练样本条件下识别性能下降的问题,提出了结合多种样本扩充方法和改进二维主成分分析(2DPCA)的人脸识别算法。通过分析各种样本扩充方法的优缺点,用多种样本扩充方法来生成虚拟样本,以充分利用单一样本所提供的信息。采用改进的2DPCA方法对生成的虚拟样本进行特征提取,对训练样本进行分块,并用类内平均值规范后的分块来构造总体散布矩阵。在ORL和Yale人脸库上的实验表明,所提出的方法在识别性能方面优于普通的2DPCA方法,优于单一的样本扩充方法。

关键词: 单样本, 人脸识别, 样本扩充, 类内平均值, 二维主成分分析(2DPCA)

Abstract: As most of the face recognition techniques will suffer serious performance drop when there is only one training sample per person, a face recognition algorithm based on sample augment methods and improved Two Dimensional Principal Component Analysis (2DPCA) was proposed. By analyzing the advantage and disadvantage of various sample augment methods, some of them were combined to synthesize virtual samples in order to make full use of the single training image. Improved 2DPCA was chosen to extract the feature of the synthetic virtual samples. The training samples were divided into sub-blocks and then the covariance matrix was constructed by these sub-blocks which were normalized by the within-class average value in each sub-block. The experimental results on ORL and Yale face database indicate that the performance of the proposed algorithm is better than those of general 2DPCA and the method only using sample augment.

Key words: single sample, face recognition, sample augment, within-class average value, Two Dimensional Principal Component Analysis (2DPCA)

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