Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D and 3D face recognition: A survey
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Fast neighborhood component analysis
Neurocomputing
IEEE Transactions on Image Processing
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Subspace methods have been very successful in face recognition. Neighborhood components analysis (NCA), one popular subspace method, however, cannot outperform discriminative common vectors (DCV) when applied to face recognition. In this paper, we proposed a Gabor feature-based fast NCA method (Gabor-FNCA). First, we extract multi-scale and multi-orientation Gabor features for more robust and enhanced face recognition. Then, we claimed that the FNCA learning problem would be ill-posed for high dimensional data dimensionality reduction. To address this problem, we first use principal component analysis (PCA) to transform the data in a low-dimensional subspace, and then use the FNCA model which including a Frobenius norm regularizer to learn the linear projection matrix. Experimental results on the ORL and FERET face datasets shows that the proposed Gabor-FNCA method is effective for face recognition.