The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Hi-index | 0.00 |
Subspace analysis is an effective approach for face recognition. Locality Preserving Projections (LPP) finds an embedding subspace that preserves local structure information, and obtains a subspace that best detects the essential manifold structure. Though LPP has been applied in many fields, it has limitations to solve recognition problem. In this paper, a novel subspace method, called Kernel Fisher Locality Preserving Projections (KFLPP), is proposed for face recognition. In our method, discriminant information with intrinsic geometric relations is preserved in subspace in term of Fisher criterion. Furthermore, complex nonlinear variations of face images, such as illumination, expression, and pose, are represented by nonlinear kernel mapping. Experi-mental results on ORL and Yale database show that the proposed method can improve face recognition performance.