Earticle

초록

영어

The paper will present a novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis (2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the most powerful discriminative method for classification. Experiments based on proposed method have been conducted on two public data sets FERET and AT&T; the results show that the proposed method could improve the classification rates.

목차

1. Introduction
 2. 2D Principal Component Analysis
  2.1. Face Model Construction
  2.2. Feature Extraction
 3. Support Vector Machine
  3.1. Classifier Construction Phase
  3.2. Classification Phase
  3.3. SVM for Face Identification
 4. Implementation and Experiments
  4.1. Experiments on AT&T database
  4.2. Experiments on FERET Database
 5. Conclusions
 References

키워드

저자

• Thai Hoang Le [ Faculty of Information Technology, HCMC University of Science Faculty of Information Sciences and Engineering, University of Canberra ]
• Len Bui [ Faculty of Information Technology, HCMC University of Science Faculty of Information Sciences and Engineering, University of Canberra ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605