Robust and discriminative dictionary learning for face recognition

Guojun Lin, Meng Yang, Linlin Shen, Mingzhong Yang, Mei Xie

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)


For face recognition, conventional dictionary learning (DL) methods have some disadvantages. First, face images of the same person vary with facial expressions and pose, illumination and disguises, so it is hard to obtain a robust dictionary for face recognition. Second, they don't cover important components (e.g., particularity and disturbance) completely, which limit their performance. In the paper, we propose a novel robust and discriminative DL (RDDL) model. The proposed model uses sample diversities of the same face image to learn a robust dictionary, which includes class-specific dictionary atoms and disturbance dictionary atoms. These atoms can well represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which improves effectively the classification capability of the dictionary. The proposed RDDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

Original languageEnglish
Article number1840004
JournalInternational Journal of Wavelets, Multiresolution and Information Processing
Issue number2
Publication statusPublished - 1 Mar 2018
Externally publishedYes


  • Dictionary learning
  • face recognition
  • sparse representation

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Applied Mathematics


Dive into the research topics of 'Robust and discriminative dictionary learning for face recognition'. Together they form a unique fingerprint.

Cite this