Color Two-Dimensional Principal Component Analysis for Face Recognition Based on Quaternion Model

摘要

The color two-dimensional principal component analysis (color 2DPCA) approach based on quaternion model is presented for color face recognition. Based on 2D quaternion matrices rather than 1D quaternion vectors, color 2DPCA combines the color information and the spatial characteristic for face recognition, and straightly computes the low-dimensional covariance matrix of the training color face images and determines the corresponding eigenvectors in a short CPU time. The image reconstruction theory is also built on color 2DPCA. The experiments on real face data sets are provided to validate the feasibility and effectiveness of the proposed algorithm.