Face recognition using nearest feature space embedding

Yin Nong Chen, Chin Chuan Han, Cheng Tzu Wang, Kuo Chin Fan

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or the NFS is embedded in the transformation through the discriminant analysis. Three factors, including class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated by several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.

Original languageEnglish
Article number5639012
Pages (from-to)1073-1086
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number6
StatePublished - 2011


  • Face recognition
  • Fisher criterion
  • Laplacianface
  • nearest feature line
  • nearest feature space


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