Relaxed 2-D Principal Component Analysis by $L_p$ Norm for Face Recognition
This is an incremental improvement for face recognition systems, potentially enhancing accuracy in biometric applications.
The paper tackled face recognition by proposing a relaxed 2-D principal component analysis method that incorporates label information and uses Lp norms, achieving higher recognition rates than state-of-the-art methods in numerical experiments.
A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ 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 $L_p$-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.