Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
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
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Faces under varying illumination, pose and non-rigid deformation are empirically thought of as a highly nonlinear manifold in the observation space. How to discover intrinsic low-dimensional manifold is important to characterize meaningful face distributions and classify them using some classifiers. In this paper, we use the Locally Linear Embedding (LLE) algorithm to reduce the dimensionality of face image. The LLE algorithm is the fast dimensionality reduction algorithm that finds local geometry in high dimensional space, and produces a projection to low dimensional space which preserves the original geometry. So, we use the Locally Linear Embedding (LLE) algorithm to reduce the dimensionality of face image for face recognition. Both frontal head images and rotated head images are investigated. Experiments on The UMIST Face Database that is a multi-view database show that the advantages of our proposed approach.