Author(s)

Haijun Kuang, Wanzhou Ye, Ze Zhu

Department of Mathematics, College of Science, Shanghai University, Shanghai, China.

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.

2DPCA, Face Recognition, Dimension Reduction, F Norm

Kuang, H. , Ye, W. and Zhu, Z. (2021) Research on Face Recognition Algorithm Based on Robust 2DPCA. Advances in Pure Mathematics, 11, 149-161. doi: 10.4236/apm.2021.112010.