Random Subspace Two-dimensional LDA for Face Recognition
This work addresses face recognition accuracy for computer vision applications, but it is incremental as it builds on prior subspace methods.
The paper tackled face recognition by developing a random subspace two-dimensional LDA (RS-2DLDA) method, which improved accuracy over an existing RS-2DPCA framework through more discriminative eigenvectors and a weighting scheme, as demonstrated on MORPH-II and ORL datasets.
In this paper, a novel technique named random subspace two-dimensional LDA (RS-2DLDA) is developed for face recognition. This approach offers a number of improvements over the random subspace two-dimensional PCA (RS2DPCA) framework introduced by Nguyen et al. [5]. Firstly, the eigenvectors from 2DLDA have more discriminative power than those from 2DPCA, resulting in higher accuracy for the RS-2DLDA method over RS-2DPCA. Various distance metrics are evaluated, and a weighting scheme is developed to further boost accuracy. A series of experiments on the MORPH-II and ORL datasets are conducted to demonstrate the effectiveness of this approach.