Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-Based Face Recognition Using Mixture-Distance
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Local feature analysis: a statistical theory for information representation and transmission
Local feature analysis: a statistical theory for information representation and transmission
Journal of Cognitive Neuroscience
Natural basis functions and topographic memory for face recognition
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Using graph algebra to optimize neighborhood for isometric mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Clustering-based nonlinear dimensionality reduction on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Performing locally linear embedding with adaptable neighborhood size on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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We proposed a novel approach for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) and Principal Component Analysis (PCA) .LLE computes a compact representation of high dimensional data combining the major advantages of linear methods, With the advantages of non-linear approaches which is flexible to learn a broad of class on nonlinear manifolds. The application of LLE, however, is limited due to its lack of a parametric mapping between the observation and the low-dimensional output. In addition, the revealed underlying manifold can only be observed subjectively. To overcome these limitations, we propose our method for recognition by fusion of LLE and Principal Component Analysis (FLLEPCA) and validate their efficiency. Experiments on CMU AMP Face EXpression Database and JAFFE databases show the advantages of our proposed novel approach.