Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Nonlinear Dimensionality Reduction with Local Spline Embedding
IEEE Transactions on Knowledge and Data Engineering
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Face recognition using discriminant locality preserving projections
Image and Vision Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Sparse transfer learning for interactive video search reranking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
m-SNE: Multiview Stochastic Neighbor Embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Motivated by the multi-channel nature of the Gabor feature representation and the success of the multiple classifier fusion, and meanwhile, to avoid careful selection of parameters for the manifold learning, we propose a face recognition framework under the multi-channel fusion strategy. The Gabor wavelet endows the algorithm in a similar way as the human visual system, to represent face features. To solve the curse of dimensionality due to multi-channel Gabor feature, as well as to preserve nonlinear labeled intrinsic structure of the sample points, the manifold learning is applied to model the nonlinear labeled intrinsic structure. Each of the filtered multi-channel Gabor features, is treated as an independent channel. Classification is performed in each channel by the component classifier and the final result is obtained using the decision fusion strategy. The experiments on three face datasets show effective and encouraging recognition accuracy compared with other existing methods.