Improved the minimum squared error algorithm for face recognition by integrating original face images and the mirror images

In order to improve the accuracy of face recognition and solve the problem of various poses and illuminations, we proposed an improved minimum squared error (IMSE) classification algorithm. Firstly, the mirror faces of the original training faces are generated through row preserved and columns flipped in the left/right direction. Secondly, the minimum squared error (MSE) algorithm is performed on both original faces and the mirror faces. Thirdly, the predicted errors of the test sample and standard class labels are obtained. In addition, the residual between the predicted labels of the test sample and each training sample can also be calculated. At last, the correct class can be determined by fusing the predicted errors and residuals. We also promoted the IMSE algorithm to the kernel MSE algorithm and proposed an improved kernel minimum squared error (IKMSE) algorithm for face recognition. The experimental results show our proposed IMSE and IKMSE algorithm are more robust than the conventional MSE and KMSE algorithm, respectively. In addition, our proposed algorithms improve the accuracy of face recognition effectively.