Earticle

초록

영어

In allusion to such problems in the traditional face recognition methods as poor recognition accuracy and dissatisfactory processing effect for directivity and anisotropic characteristic in face data, lasso regularized Gabor shearlet face multivariate sparse function approximation algorithm is proposed in this article. Firstly, Gabor improved shearlet algorithm is adopted at the level of the face-image biological signals for the sparse expansion representation of the face data characteristics, and meanwhile this algorithm is also adopted to extract the geometrical characteristics of the expansion face with directivity and anisotropic characteristic. Secondly, in order to balance the algorithm effect, lasso regularization theory is introduced therein to control and weigh the relation between the fidelity and the smoothness of the face data. Finally, the corresponding simulation experiment is carried out to compare the proposed algorithm and the existing algorithms in the standard test database in order to verify the advantages of the proposed algorithm in the aspect of face recognition accuracy and efficiency.

목차

Abstract
 1. Introduction
 2. Classification Algorithm Based on Sparse Representation
 3. Regularized Gabor Shearlet
  3.1. Algorithm Framework
  3.2. Model Shearlet Characteristic Extraction
  3.3. Description of the Proposed Algorithm
 4. Experiment and Analysis
  4.1. Experiment Conditions
  4.2. Recognition Accuracy
 5. Conclusion
 References

키워드

저자

• LI Dong-Rui [ Department of Computer, Guangdong AIB Polytechnic College, Guangzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605