Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
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This paper presents a novel scheme for face feature extraction, namely, the generalized two-dimensional Fisher's linear discriminant (G-2DFLD) method The G-2DFLD method is an extension of the 2DFLD method for feature extraction Like 2DFLD method, G-2DFLD method is also based on the original 2D image matrix However, unlike 2DFLD method, which maximizes class separability either from row or column direction, the G-2DFLD method maximizes class separability from both the row and column directions simultaneously In G-2DFLD method, two alternative Fisher's criteria have been defined corresponding to row and column-wise projection directions The principal components extracted from an image matrix in 2DFLD method are vectors; whereas, in G-2DFLD method these are scalars Therefore, the size of the resultant image feature matrix is much smaller using G-2DFLD method than that of using 2DFLD method The proposed G-2DFLD method was evaluated on two popular face recognition databases, the AT&T (formerly ORL) and the UMIST face databases The experimental results show that the new G-2DFLD scheme outperforms the PCA, 2DPCA, FLD and 2DFLD schemes, not only in terms of computation times, but also for the task of face recognition using a multi-class support vector machine.