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.