Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Journal of Cognitive Neuroscience
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The manifold learning methods can discover the varying intrinsic features in face image space. However, in order to efficiently solve face image recognition problem with an image database, the extraction of discriminative features should be firstly considered. This paper proposes a new discriminative manifold learning method for face recognition. Besides like the recently proposed local perserving projectioin and local discriminative embedding algorithms which can preserve the local structure similarity in the face submanifold, our method emphasizes the discriminative property of embedding much more by a proposed Fisher Manifold Discriminant Embedding (Fisher MDE) criterion to build an object function and achieve the maximum. Experimental results on three open face datasets indicate the proposed method achieves lower error rates and provides a promising performance.