Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
DT-CWT Feature Combined with ONPP for Face Recognition
Computational Intelligence and Security
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In this paper, a novel subspace learning method called Manifold Regularized Orthogonal Discriminant Analysis (MRODA) is first proposed. Based on within-class local geometry preservation and Least Square regression framework for LDA, MRODA can encode both the local geometry and discriminant structures of face data manifolds, and can address the small sample size problem through pseudo-inverse resolution. The transform vectors are orthogonalized to improve their discriminatory performance. Based on the selected Dual-Tree Complex Wavelet Transform features, an approach for face recognition based on the fusion of spatial and frequency features is developed. Experimental results on ORL, Yale and AR face databases show the effectiveness of the proposed approach.