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
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisherpalms based palmprint recognition
Pattern Recognition Letters
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
A Direct Locality Preserving Projections (DLPP) Algorithm for Image Recognition
Neural Processing Letters
Palmprint recognition with improved two-dimensional locality preserving projections
Image and Vision Computing
Extensions of Manifold Learning Algorithms in Kernel Feature Space
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A New Solution Scheme of Unsupervised Locality Preserving Projection Method for the SSS Problem
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Fusion of classifiers for illumination robust face recognition
Expert Systems with Applications: An International Journal
A survey of palmprint recognition
Pattern Recognition
Handprint Recognition: A Novel Biometric Technology
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
LPP solution schemes for use with face recognition
Pattern Recognition
Face Recognition Using Kernel UDP
Neural Processing Letters
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
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Locality preserving projections (LPP) is a new subspace feature extraction method which seeks to preserve the local structure and intrinsic geometry of the data space. As the LPP model is linear, it may fail to extract the nonlinear features. This paper proposes to address this problem using an alternative formulation, kernel locality preserving projections (KLPP). Our algorithm consists of two steps: kernel principal component analysis (KPCA) plus LPP. We provide an outline for implementing KLPP. Experiments on the ORL face database and PolyU palmprint database demonstrate the effectiveness of the proposed algorithm.