Corpus ID: 15688865

Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

@article{Wang2012KernelPC,
  title={Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models},
  author={Q. Wang},
  journal={ArXiv},
  year={2012},
  volume={abs/1207.3538}
}
  • Q. Wang
  • Published 2012
  • Computer Science
  • ArXiv
  • Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. [...] Key Method We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experimental results to compare the performance of kernel PCA and standard PCA for classification problems. We also implement the kernel PCA-based ASMs, and use it to construct human face models.Expand Abstract
    An Overview of Non-Linear Kernel Functions for Solving the Human Face Recognition Problem
    Kernel Principle Component Analysis in Face Recognition System: A Survey
    10
    A Comparative Study on PCA and KPCA Methods for Face Recognition
    2
    Recognition of Facial Expression via Kernel PCA Network
    1

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