Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image

@article{Xie2006GaborbasedKP,
  title={Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image},
  author={Xudong Xie and K. Lam},
  journal={IEEE Transactions on Image Processing},
  year={2006},
  volume={15},
  pages={2481-2492}
}
  • Xudong Xie, K. Lam
  • Published 2006
  • Computer Science, Medicine, Mathematics
  • IEEE Transactions on Image Processing
In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable… Expand
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