2018/03/10 by Meixiang Zhao, Zhao, Meixiang, Zhigang Jia +3
Computer Science · Earth and Planetary Sciences · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Remote Sensing and Land Use #Remote-Sensing Image Classification
paper · pdf · doi:10.48550/arxiv.1803.03837
openalex publication_date 2018/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A sample-relaxed two-dimensional color principal component analysis (SR-2DCPCA) approach is presented for face recognition and image reconstruction based on quaternion models. A relaxation vector is automatically generated according to the variances of training color face images with the same label. A sample-relaxed, low-dimensional covariance matrix is constructed based on all the training samples relaxed by a relaxation vector, and its eigenvectors corresponding to the r largest eigenvalues are defined as the optimal projection. The SR-2DCPCA aims to enlarge the global variance rather than to maximize the variance of the projected training samples. The numerical results based on real face data sets validate that SR-2DCPCA has a higher recognition rate than state-of-the-art methods and is efficient in image reconstruction.