Robust 2DPCA and Its Application
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.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)