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arXiv · 1803.03837

Sample-Relaxed Two-Dimensional Color Principal Component Analysis for Face Recognition and Image Reconstruction

Meixiang Zhao·Zhigang Jia·Dunwei Gong
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Abstract

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.

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Meixiang Zhao, Zhigang Jia, Dunwei Gong. 2018-03-10. Sample-Relaxed Two-Dimensional Color Principal Component Analysis for Face Recognition and Image Reconstruction. https://arxiv.org/abs/1803.03837

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