Face recognition and gender classification using orthogonal nearest neighbour feature line embedding regular paper

Gang Feng Ho, Ying Nong Chen, Chin Chuan Han, Kuo Chin Fan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, a novel manifold learning algorithm for face recognition and gender classification - orthogonal nearest neighbour feature line embedding (ONNFLE) - is proposed. Three of the drawbacks of the nearest feature space embedding (NFSE) method are solved: the extrapolation/interpolation error, high computational load and non-orthogonal eigenvector problems. The extrapolation error occurs if the distance from a specified point to one line is small when that line passes through two farther points. The scatter matrix generated by the invalid discriminant vectors does not efficiently preserve the locally topological structure - incorrect selection reduces recognition. To remedy this, the nearest neighbour (NN) selection strategy was used in the proposed method. In addition, the high computational load was reduced using a selection strategy. The last problem involved solving the nonorthogonal eigenvectors found with the NFSE algorithm. The proposed algorithm generated orthogonal bases possessing more discriminating power. Experiments were conducted to demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish
Article number105
JournalInternational Journal of Advanced Robotic Systems
Volume9
DOIs
StatePublished - 4 Oct 2012

Keywords

  • Extrapolation
  • Face recognition
  • Gender classification
  • Nearest feature line
  • Orthogonal basis

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