Robust kernel representation with statistical local features for face recognition

Meng Yang, Lei Zhang, Chi Keung Simon Shiu, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

93 Citations (Scopus)


Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.
Original languageEnglish
Article number6471239
Pages (from-to)900-912
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number6
Publication statusPublished - 17 Apr 2013


  • Collaborative representation
  • face recognition
  • robust kernel representation
  • statistical local feature

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this