Kernel nearest-farthest subspace classifier for face recognition

Linlin Tang, Zuohua Li, Jingyong Su, Huifen Lu, Zhangyan Li, Zhen Pang, Yong Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper, a novel classifier named Kernel Nearest-Farthest Subspace (KNFS) classifier is proposed for face recognition. Inspired by the kernel-based classifier and the Nearest-Farthest Subspace (NFS) classifier, KNFS can make the sample points to be linear separable by utilizing the kernel function to map linear inseparable sample points in low-dimensional space to high-dimensional kernel space. And it can improve the recognition accuracy of crossed sample points between classes. The algorithm provides the highest reported recognition accuracy on AR and AT&T database. The results are comparable with many other state-of-art face recognition algorithms.

Original languageEnglish
Pages (from-to)32007-32021
Number of pages15
JournalMultimedia Tools and Applications
Issue number22
StatePublished - Nov 1 2019


  • Face recognition
  • Kernel function
  • Nearest-farthest subspace classifier


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