Principal component analysis (PCA) plays an important role in image processing and machine learning because of its simplicity and effectiveness. In the image analysis domain

a 2D image is usually reshaped into a 1D vector

thus leading to high dimensionality and damage to intrinsic spatial information. Two-dimensional PCA (2DPCA) successfully solves the high-dimensional problem caused by vector-based methods and can significantly preserve spatial information. However

2DPCA minimizes the minimum mean square error (MSE) and is sensitive to outliers. In this paper

a robust 2DPCA algorithm based on the maximum correntropy criterion (MCC) is proposed for face recognition. The proposed method can reduce the effect of outliers and improve recognition accuracy remarkably. In 2DPCA-MCC

the objective function is computed on the basis of the MCC

which is a useful measurement to handle non-zero-mean data. Given that the correntropy objective is a nonlinear optimization problem

which can be efficiently solved by the half-quadratic (HQ) optimization framework in an iterative manner

this study derives the algorithm to solve the correntropy objective on the basis of MCC. At each iteration

the complex nonlinear optimization problem is reduced to a weighed PCA problem and the correntropy objective is increased systematically. Thus

this new 2DPCA algorithm can handle non-centered data and can naturally estimate the data mean. Face recognition experiments have been implemented on ORL databases. In the experiment

5 training images are randomly selected from all 10 face images for each individual and the data is not zero-centered. To test the robustness to outliers of the proposed method

20

40

and 60 percent of the 5 training images are occluded with random location and random size dots. The proposed algorithm can separately improve the recognition accuracy by nearly 10

19

and 30 percent compared to the original two-dimensional PCA algorithms at 20

40

and 60 percent images with outliers of all training images for each individual. The minimum MSE is a simple measurement to compute the objective function. However

the MSE-based algorithm can only handle zero-mean data. To address this problem

this study proposes a new robust 2DPCA algorithm based on the MCC

which is a useful measurement for handling non-centered data. The algorithm uses the HQ optimization framework to solve the correntropy objective

which is a complicated nonlinear optimization problem. The experimental results on the ORL database prove that the proposed 2DPCA-MCC algorithm is more robust to outliers and can achieve better recognition results than the original MSE-based 2DPCA algorithms without the limitation of zero-mean data.