M2D-QPCA: An Improved Quaternion Principal Component Analysis Method for Color Face Recognition

Principal component analysis (PCA) is one of the successful dimensionality reduction approaches for color face recognition. For various PCA methods, the experiments show that the contribution of eigenvectors is different and different weights of eigenvectors can cause different effects. Based on this, a modified and simplified color two-dimensional quaternion principal component analysis (M2D-QPCA) method is proposed along the framework of the color two-dimensional quaternion principal component analysis (2D-QPCA) method and the improved two-dimensional quaternion principal component analysis (2D-GQPCA) method. The shortcomings of 2D-QPCA are corrected and the CPU time of 2D-GQPCA is reduced. The experiments on two real face data sets show that the accuracy of M2D-QPCA is better than that of 2D-QPCA and other PCA-like methods and the CPU time of M2D-QPCA is less than that of 2D-GQPCA.

Recognition of Colored Face, Based on an Improved Color Local Binary Pattern

In this paper, a novel feature extraction method based on an improved color local binary pattern (LBP) is proposed for color face recognition. Firstly, in a given neighborhood of every pixel, we choose some sampling points from three color channels simultaneously and the numbers of the sampling points from every channel may be different. Secondly, we use a new rule to select the threshold which does not always locate in the geometrical center of the given neighborhood. Thirdly, in order to excavate the potential of the proposed sampling method, we use the [Formula: see text]-uniform LBP to obtain the binary code of each pixel. In addition, we embed the Hamming distance into our method for improving the recognition rate of the proposed method. For evaluating the performance of our method, we implement the proposed method and several related methods on five public face databases: FERET, CMU-PIE, Georgia, FEI and Asian databases. Experimental results show that our method possesses higher recognition rates and lower computational cost than other related color face recognition methods.