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A sample-relaxed two-dimensional color principal component analysis(SR-2DCPCA) approach is presented for face recognition and image reconstructionbased on quaternion models. A relaxation vector is automatically generatedaccording to the variances of training color face images with the same label. Asample-relaxed, low-dimensional covariance matrix is constructed based on allthe training samples relaxed by a relaxation vector, and its eigenvectorscorresponding to the $r$ largest eigenvalues are defined as the optimalprojection. The SR-2DCPCA aims to enlarge the global variance rather than tomaximize the variance of the projected training samples. The numerical resultsbased on real face data sets validate that SR-2DCPCA has a higher recognitionrate than state-of-the-art methods and is efficient in image reconstruction.

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