›› 2013,Vol. 28 ›› Issue (2): 322-328.doi: 10.1007/s11390-013-1333-5

所属专题： Artificial Intelligence and Pattern Recognition

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

人脸识别中的结合图像分量随机贪婪方法和子空间方法的特征优化抽取研究

Mathu Soothana S. Kumar Retna Swami1 and Muneeswaran Karuppiah2   

  • 收稿日期:2012-01-22 修回日期:2013-01-11 出版日期:2013-03-05 发布日期:2013-03-05

Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

Mathu Soothana S. Kumar Retna Swami1 and Muneeswaran Karuppiah2   

  1. 1 Department of Information Technology, Noorul Islam University, Thuckalay 629180, India;
    2 Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India
  • Received:2012-01-22 Revised:2013-01-11 Online:2013-03-05 Published:2013-03-05

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本文提出了一种新的基于Gabor小波和主成分分析以及多重判别分析的图形特征抽取的统一框架。在此框架下, 贪婪方法被用于从最优随机图像分量中抽取特征。并且, 线性问题求解将特征向量投影到子空间以进行降维。面部特征抽取的主要处理过程在于Gabor过滤、主成分分析和多重判别分析。在FERET,ORL和YALE面部数据集上的实验表明, 结合最优随机图像分量抽取和多重判别分析得到的结果优于不使用子空间和投影的特征抽取方法(如最优随机图像分量抽取和主成分分析)。利用30%的训练数据集, 本文提出的特征抽取方法分别在FERET,ORL和YALE面部数据集上得到了96.25%,99.44%和100%的识别准确率。相比较于已有的文献所提出的其他面部特征抽取方法, 本文所提出方法得到实验结果具有明显的性能提升。

Abstract: An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis (PCA) and multiple discriminant analysis (MDA) is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection (ORICS) plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.

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