Robust 2DPCA and Its Application
Qianqian Wang, Quanxue Gao · 2016
Two-dimensional Principal Component Analysis (2DP-CA) has been widely used for face image representation and recognition. However, 2DPCA, which is based on F-norm square, is sensitive to the presence of outliers. To enhance the robustness of 2DPCA model, we proposed a novel Robust 2DPCA objective function, called R-2DPCA. The criterion of R-2DPCA is maximizing the covariance of data in the projected subspace, while minimizing the reconstruction error of data. In addition, we use the efficient non-greedy optimization algorithms solving our objective function. Extensive experiments are done on the AR, CMU-PIE, Extended Yale B face image databases, and results illustrate that our method is more effective and robust than other robust 2DPCA algorithms, such as L1-2DPCA, L1-2DPCA-S, and N-2DPCA.