Earticle

초록

영어

Facial recognition (FR) is a challenging area of research due to difficulties with robust FR when the number of training samples is very small. The state-of-the-art sparse representation-based classification (SRC) shows very excellent FR performance. However, the recognition rate of SRC will drop dramatically when the number of training samples per class is very limited. To solve these issues, we propose a weighted multi-classifier optimization and sparse representation based (WMSRC) method for FR, which efficiently combines the local and global characteristics of face images. A face image is firstly divided into continuous but non-overlapped blocks by multi-resolution based blocking and each block is sparsely represented over the corresponding set of blocks of all training samples. The multi-scale SRC classifiers are then established and associated with different weights based on sub-block dictionary learning. According to the multiple voting results of the classifiers, the weights of multi-classifiers are optimized by a least-squares optimization equation with 2l-norm regularization. Finally, the classification results of all the blocks are combined by a weighted fusion criterion. Our experiments show that the WMSRC algorithm outperforms many existing block-based sparse representation classification algorithms, especially for FR when the available training samples per subject are very limited.

목차

Abstract
 1. Introduction
 2. Sparse Representation-based Classification for FR
 3. Sparse Representation based Multi-Classifier Weighted Optimization and Fusion for FR
  3.1. Total Flow of WMSRC Method
  3.2. Multi-resolution Block and Dimensionality Reduction of Face Image
  3.3. K-SVD-based dictionary learning of sub-blocks
  3.4. Establishment of Multi-classifier
  3.5. Multi-classifier Weighted Optimization
  3.6. Multi-classifier Fusion
 4. Experimental Analysis
  4.1. ORL Database
  4.2. YaleA Database
  4.3. AR Database
  4.4. Algorithm Efficiency
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Deng Nan [ Institute of control science and engineering, University of Science and Technology Beijing ]
• Zhengguang Xu [ Institute of control science and engineering, University of Science and Technology Beijing ]
• ShengQin Bian [ Institute of control science and engineering, University of Science and Technology Beijing ]

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