Earticle

초록

영어

How to extract the robust discrimination features is the key of face recognition (FR). Local binary pattern is one of the most widely used feature extracting method in FR for its comprehensive representation of the visual content of face image. However, the feature vector extracted by LBP is usually very high-dimensional and maybe contains information redundancy. To deal with the drawback of LBP, a novel nonlinear version of LBP is presented. The main idea is firstly all the feature vectors extracted by LBP are mapped into a feature space by a nonlinear mapping, and then the mapped features are expressed using the corresponding projection vectors. Lastly, FR is performed based on the projection vectors. Compared with LBP, the new method has two advantages. Firstly, it can capture the nonlinear information of the feature vector extracted by LBP. Secondly, it avoids the complex expression of the nonlinear mapping. The experimental results on two public standard visual face datasets demonstrate the proposed method is superior to LBP in recognition accuracy while its computational complexity is considerably reduced.

목차

Abstract
 1. Introduction
 2. Description of LBP
 3. The Nonlinear Version of LBP (NLBP)
  3.1. The Main Idea of NLBP
  3.2. How to Get a Standard Orthogonal Basis
 4. Experiments Analysis
 5. Conclusion
 References

키워드

저자

• Zhanghongyi [ School of Optoelectronic & Communication, School of Computer Science and Technology Engineering, Xiamen University of Technology, Shandong Institute of Business and Technology Xiamen, China, Yantai, China ]
• Zhao Feng [ School of Optoelectronic & Communication, School of Computer Science and Technology Engineering, Xiamen University of Technology, Shandong Institute of Business and Technology Xiamen, China, Yantai, China ]

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