Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Face recognition using kernel scatter-difference-based discriminant analysis
IEEE Transactions on Neural Networks
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A new kernel discriminant analysis algorithm, called Kernel-based Enhanced Maximum Margin Criterion (KEMMC), is presented for extracting features from high-dimensional data space. In this paper, the EMMC is firstly proposed which attempts to maximize the average margin between classes after dimensionality reduction transformation. In our method, a weighted matrix is introduced and the local property is taken into account so that the data points of neighboring classes can be mapped far away. Moreover, the regularized technique is employed to deal with small sample size problem. As EMMC is a linear method, it is extended to a nonlinear form by mapping the input space to a high-dimensional feature space which can make the mapped features linearly separable. Extensive experiments on handwritten digit image and face image data demonstrate the effectiveness of the proposed algorithm.