计算机科学与探索 ›› 2009, Vol. 3 ›› Issue (5): 539-549.DOI: 10.3778/j.issn.1673-9418.2009.05.009

• 学术研究 • 上一篇    下一篇

优化线性辨别分析在人脸识别中的应用

杨文新1+,饶淑琴1,王继娜1,印 鉴1,陈 健2   

  1. 1. 中山大学 计算机科学与技术系,广州 510275
    2. 华南理工大学 软件学院,广州 510006
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-09-15 发布日期:2009-09-15
  • 通讯作者: 杨文新

Face Recognition Using Clustering Based Optimal Linear Discriminant Analysis

YANG Wenxin1+,RAO Shuqin1, WANG Jina1, YIN Jian1, CHEN Jian2   

  1. 1. Department of Computer Science, Sun Yat-Sen University, Guangzhou 510275, China
    2. School of Software, South China University of Technology, Guangzhou 510006, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-09-15 Published:2009-09-15
  • Contact: YANG Wenxin

摘要: 国内外学者研究发现,类间方差最大化的方向与类内方差最大化的方向之间的角度对传统的线性辨别分析方法的准确性影响显著,并且,当这两个方向平行的时候,传统的线性方法往往不能得到很好的结果。经过的研究和实验,发现传统线性方法的准确性与类间方差和类内方差之间的角度没有直接的决定关系,它的最大问题在于线性地对类间方差和类内方差的加和不能完全保留类别之间的辨别信息。提出了一种优化的线性辨别分析的方法(OLDA)来解决这个问题。首先,引入了辨别能量的概念,能够给任意两个类之间的辨别信息赋予同样的权重;其次,引入了一种梯度下降的算法来计算最终的判别向量,并且加速迭代算子的引入能够更加有效地解决运算复杂度的问题。最后,为了解决非线性问题,预先的聚类算法能够将非线性问题转化成为线性问题,从而使数据集能够被有效地分辨出来。采用了一个人脸数据集和一个虚拟数据集进行了实现,实验结果表明提出的优化辨别分析的方法能够有效地解决数据集的分类问题。

关键词: 人脸识别, 线性辨别分析, 特征分解, 辨别能量, 聚类分析

Abstract: Recently, current researches indicated that, the angle between the eigenvector corresponding to the largest eigenvalue of the inter-class covariance and the eigenvector corresponding to the largest eigenvalue of the intra-class covariance is more crucial to the performance of traditional linear discriminant methods, furthermore, if the two eigenvectors are parallel; the final results may be disputable. However, upon careful scrutiny on his assertion, conclude that the angle between the two eigenvectors is less decisive to the performance, more over; the main drawback of traditional linear methods is the inter-class covariance cannot precisely reflect the discriminant information. Simply maximizing the inter-class covariance in the principle component space may induce the losing of adjacent class-pair’s contribution. Therefore, propose the optimal linear discriminant analysis (OLDA) method, which distributes equivalent authority for each class-pair by employing “discriminative power”. Besides, employ the gradient scheme to derive the feature vectors, and a constraint condition is introduced to evaluate the convergence speed. Thirdly, to address the multimodal problem, the pre-clustering mechanism is adopted to ameliorate the nonlinear structure. Apply this method on a practical face database and a virtual database, the experimental results show the promise of this method.

Key words: face recognition, linear discriminant analysis, eigen-decomposition, discriminant power, clustering

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