Earticle

초록

영어

Among the various biometric methods, face recognition has become one of the most challenging tasks in the pattern recognition field during the past decades. An integrated algorithm for face recognition is proposed based on the respective advantages of wavelets transform (WT), 2D Principle Component Analysis (PCA) and Support Vector Machines (SVM) in this paper. At first stage, the original images are decomposed into low frequency images by applying wavelets transform, the high-frequency components were ignored, while the low-frequency components which contains the primary information can be obtained. And then 2D PCA algorithm is used to deal with feature extraction. After generating feature vector, distance classifier and SVM are used for classification stage. Experiments with two face databases show that the proposed method has accuracy and robust for face recognition.

목차

Abstract
 1. Introduction
 2. Decomposing Images using 2D-DWT
 3. Feature Extraction using 2D PCA Algorithm
 4. Faces classifier using SVM approach
  4.1. Support Vector Machines algorithm
  4.2. SVM face classifier combining with Wavelets and 2DPCA
 5. Experimental Results
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Wenkai Xu [ Department of Information Communications and Engineering, Tongmyong University, Busan, Korea ]
• Eung-Joo Lee [ Department of Information Communications and Engineering, Tongmyong University, Busan, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605