ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2018, Vol. 55 ›› Issue (8): 1760-1772.doi: 10.7544/issn1000-1239.2018.20180364

Special Issue: 2018数据挖掘前沿进展专题

Previous Articles     Next Articles

Fast Low-Rank Shared Dictionary Learning with Sparsity Constraints on Face Recognition

Tian Ze, Yang Ming,Li Aishi   

  1. (College of Computer Science and Technology, Nanjing Normal University, Nanjing 210023)
  • Online:2018-08-01

Abstract: Dictionary learning is one of the most important feature representation methods. It has a wide range of applications in face recognition and other aspects. It is particularly suitable for solving face recognition problems under the change of pose, and has attracted much attention from many researchers. In order to enhance the discriminative ability of dictionary, researchers have put forward a large number of dictionary learning models in combination with domain knowledge and anti-noise strategies, including the recently proposed methods for simultaneous dimensionality reduction and dictionary learning, but these methods focus on the specific-class samples and fail to consider the sharing information between training samples. Therefore, we propose a fast low-rank shared dictionary learning with sparsity constraints approach. The method learns dimensionality reduction and dictionary jointly, and embeds Fisher discriminant criteria to obtain specific-class dictionary and coding coefficients. At the same time, we enforce a low-rank constraint to obtain the low-rank shared dictionary to enhance the discriminative ability of dictionary and coding coefficients. In addition, the Cayley transform is used to protect the orthogonality of the projection matrix to catch a compact feature set. Face recognition experiments on AR, Extended Yale B, CMU PIE, and FERET datasets demonstrate the superiority of our approach. The experimental results show that the proposed method has strong robustness to face recognition under facial expression changes, and plays an inhibitory role in lighting. It is especially suitable for solving small sample problems under illumination and expression changes.

Key words: face recognition, dictionary learning, sparsity constraints, low-rank models, shared features

CLC Number: