Sample diversity, discriminative and comprehensive dictionary learning for face recognition

Guojun Lin, Meng Yang, Linlin Shen, Weicheng Xie, Zhonglong Zheng

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


For face recognition, conventional dictionary learning (DL) methods have disadvantages. In the paper, we propose a novel robust, discriminative and comprehensive DL (RDCDL) model. The proposed model uses sample diversities of the same face image to make the dictionary robust. The model includes class-specific dictionary atoms and disturbance dictionary atoms, which can well represent the data from different classes. Both the dictionary and the representation coefficients of data on the dictionary introduce discriminative information, which improves effectively the discrimination capability of the dictionary. The proposed RDCDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art sparse representation and dictionary learning methods for face recognition.

Original languageEnglish
Title of host publicationBiometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings
EditorsShiguang Shan, Zhisheng You, Jie Zhou, Weishi Zheng, Yunhong Wang, Zhenan Sun, Jianjiang Feng, Qijun Zhao
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319466538
Publication statusPublished - 2016
Externally publishedYes
Event11th Chinese Conference on Biometric Recognition, CCBR 2016 - Chengdu, China
Duration: 14 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9967 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Chinese Conference on Biometric Recognition, CCBR 2016


  • Dictionary learning
  • Face recognition
  • Sparse representation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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