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Zhang, D. and Zhou, Z.H. (2005) (2D)²PCA: Two-Directional Two-Dimensional PCA for Efficient Face Representation and Recognition. Neurocomputing, 69, 224-231.
https://doi.org/10.1016/j.neucom.2005.06.004

has been cited by the following article:

• TITLE: Research on Face Recognition Algorithm Based on Robust 2DPCA

  AUTHORS: Haijun Kuang, Wanzhou Ye, Ze Zhu

  KEYWORDS: 2DPCA, Face Recognition, Dimension Reduction, F Norm

  JOURNAL NAME: Advances in Pure Mathematics, Vol.11 No.2, February 22, 2021

  ABSTRACT: As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers. Therefore, this paper proposes a new 2DPCA algorithm based on angel-2DPCA. To reduce the reconstruction error and maximize the variance simultaneously, we choose F norm as the measure and propose the Fp-2DPCA algorithm. Considering that the image has two dimensions, we offer the Fp-2DPCA algorithm based on bilateral. Experiments show that, compared with other algorithms, the Fp-2DPCA algorithm has a better dimensionality reduction effect and better robustness to outliers.