Earticle

초록

영어

In this paper a novel face recognition approach based on Adaptive Principal Component Analysis (APCA) and de-noised database is produced. The aim of our approach is to overcome PCA disadvantages especially the two limitations of discriminatory power poverty and the computational load complexity, by producing a new adaptive PCA based on single level 2-D discrete wavelet transform using Daubachies filter mode. All face images in ORL database are transformed to JPG file format and are de-noised by Haar wavelet at level 10 of decomposition; the goal is to exhibit the advantage of wavelet over compressed JPG files instead of using origin PGM file format. As a result , our adaptive approach produced good performance in raising the accuracy ratio and reducing both the time and the computation complexities when compared with four other methods represented by standard statistical PCA, Kernel PCA, Gabor PCA and PCA with Back propagation Neural Network (BPNN).

목차

Abstract
 1. Introduction
 2. Related Work
 3. Background
  3.1. Principal Component Analysis
  3.2. 2-D Discrete Wavelet Transform
 4. The Framework of the Adaptive Approach
  4.1. The Proposed APCA
  4.2. The Proposed De-noised Database by Haar Wavelet Filter
 5. Experiments and Results
 6. Discussion
 7. Conclusions
 Acknowledgments
 References

키워드

저자

• Isra’a Abdul-Ameer Abdul-Jabbar [ School of Computer and Information, Hefei University of Technology, Hefei 230009, People’s Republic of China, Computer Science Department, University of Technology, Baghdad, Iraq ]
• Jieqing Tan [ School of Computer and Information, Hefei University of Technology, Hefei 230009, People’s Republic of China ]
• Zhengfeng Hou [ School of Computer and Information, Hefei University of Technology, Hefei 230009, People’s Republic of China ]

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