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Sample-Relaxed Two-Dimensional Color Principal Component Analysis for Face Recognition and Image Reconstruction
A sample-relaxed two-dimensional color principal component analysis (SR-2DCPCA) approach is presented for face recognition and image reconstruction based on quaternion models.
Relaxed 2-D Principal Component Analysis by $L_p$ Norm for Face Recognition
A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition.
Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition
To enhance the generalization ability of extracted features, a novel relaxed 2DPCA (R2DPCA) is proposed with a new ridge regression model.
Generalized Two-Dimensional Quaternion Principal Component Analysis with Weighting for Color Image Recognition
One of the most powerful methods of color image recognition is the two-dimensional principle component analysis (2DQPCA) approach, which is based on quaternion representation and preserves color information very well.
Data-Driven Bilateral Generalized Two-Dimensional Quaternion Principal Component Analysis with Application to Color Face Recognition
A new data-driven bilateral generalized two-dimensional quaternion principal component analysis (BiG2DQPCA) is presented to extract the features of matrix samples from both row and column directions.
