2 research outputs found
Relaxed 2-D Principal Component Analysis by Norm for Face Recognition
A relaxed two dimensional principal component analysis (R2DPCA) approach is
proposed for face recognition. Different to the 2DPCA, 2DPCA- and G2DPCA,
the R2DPCA utilizes the label information (if known) of training samples to
calculate a relaxation vector and presents a weight to each subset of training
data. A new relaxed scatter matrix is defined and the computed projection axes
are able to increase the accuracy of face recognition. The optimal -norms
are selected in a reasonable range. Numerical experiments on practical face
databased indicate that the R2DPCA has high generalization ability and can
achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure
Generalized Two-Dimensional Quaternion Principal Component Analysis with Weighting for Color Image Recognition
A generalized two-dimensional quaternion principal component analysis
(G2DQPCA) approach with weighting is presented for color image analysis. As a
general framework of 2DQPCA, G2DQPCA is flexible to adapt different constraints
or requirements by imposing norms both on the constraint function and
the objective function. The gradient operator of quaternion vector functions is
redefined by the structure-preserving gradient operator of real vector
function. Under the framework of minorization-maximization (MM), an iterative
algorithm is developed to obtain the optimal closed-form solution of G2DQPCA.
The projection vectors generated by the deflating scheme are required to be
orthogonal to each other. A weighting matrix is defined to magnify the effect
of main features. The weighted projection bases remain the accuracy of face
recognition unchanged or moving in a tight range as the number of features
increases. The numerical results based on the real face databases validate that
the newly proposed method performs better than the state-of-the-art algorithms.Comment: 15 pages, 15 figure