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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.
Efficient Robust Watermarking Based on Quaternion Singular Value Decomposition and Coefficient Pair Selection
In this way, compared with conventional QSVD, the proposed watermarking strategy avoids more modifications to a single color image layer and a better visual quality of the watermarked image is observed.
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.
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.
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.
