Extended JSSL for Multi-Feature Face Recognition via Intra-Class Variant Dictionary

Guojun Lin, Qinrui Zhang, Shunyong Zhou, Xingguo Jiang, Hao Wu, Hairong You, Zuxin Li, Ping He, Heng Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review


This paper focuses on how to represent the testing face images for multi-feature face recognition. The choice of feature is critical for face recognition. The different features of the sample contribute differently to face recognition. The joint similar and specific learning (JSSL) has been effectively applied in multi-feature face recognition. In the JSSL, although the representation coefficient is divided into the similar coefficient and the specific coefficient, there is the disadvantage that the training images cannot represent the testing images well, because there are probable expressions, illuminations and disguises in the testing images. We think that the intra-class variations of one person can be linearly represented by those of other people. In order to solve well the disadvantage of JSSL, in the paper, we extend JSSL and propose the extended joint similar and specific learning (EJSSL) for multi-feature face recognition. EJSSL constructs the intra-class variant dictionary to represent the probable variation between the training images and the testing images. EJSSL uses the training images and the intra-class variant dictionary to effectively represent the testing images. The proposed EJSSL method is perfectly experimented on some available face databases, and its performance is superior to many current face recognition methods.

Original languageEnglish
Article number9456884
Pages (from-to)91807-91819
Number of pages13
JournalIEEE Access
Publication statusPublished - Jun 2021


  • face recognition
  • image classification
  • multi-feature
  • Sparse representation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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