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Computer Science > Computer Vision and Pattern Recognition

arXiv:1803.03837 (cs)
[Submitted on 10 Mar 2018]

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

Authors: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.
Comments: 18 pages, 7 figures
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1803.03837 [cs.CV]
  (or arXiv:1803.03837v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1803.03837

Submission history

From: Zhigang Jia [view email]
[v1] Sat, 10 Mar 2018 16:49:54 UTC (1,028 KB)
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