Earticle

초록

영어

In order to improve the robustness of face identification with the changes of illumina-tion, expression and facial alteration, a new facial feature extraction algorithm based on weighted adaptive lifting wavelet(ALW) scheme and pulse-coupled neural network (PCNN) is involved in this paper. The face images are decomposed into several subbands by weighted adaptive lifting scheme. Then the PCNN is utilized to decompose each weighted subbands into a series of binary images, the entropies of which are calculated and regarded as facial features. Experimental results show that the method yields a good robustness against the illumination, expression and facial variability and reduces the computer burden.

목차

Abstract
 1. Introduction
 1. Lifting Wavelet Scheme and Pulsed-Coupled Neural Network
  1.1. Lifting Scheme
  1.2. Pulse-Coupled Neural Network
 2. The Proposed Algorithm
  2.1. Weighted Lifting Wavelet Scheme
  2.2. The Proposed Method
 3. Simulation and Analysis
  3.1. Experiment in ORL
  3.2. Experiment in YALE
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Junhua Liang [ School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, Hebei, China ]
• Zhisheng Zhao [ School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, Hebei, China ] Corresponding author
• Xiao Zhang [ School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, Hebei, China ]
• Xuan Wang [ School of Physics and Information Technology, Shaanxi Normal University, Xi’ an 710062, Shaanxi, China ]
• Yang Liu [ School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, Hebei, 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