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
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
General Averaged Divergence Analysis
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Biologically inspired feature manifold for scene classification
IEEE Transactions on Image Processing
Robust spectral regression for face recognition
Neurocomputing
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Dimension reduction algorithms have attracted a lot of attentions in face recognition and human gait recognition because they can select a subset of effective and efficient discriminative features. In this paper, we apply the Discriminative Geometry Preserving Projections (DGPP), a new subspace learning algorithm to address these problems. DGPP models both the intraclass geometry and interclass discrimination. Meanwhile, DGPP will not meet the undersampled problem. Thoroughly empirical studies on YALE face database, UMIST face database, FERET face database and USF Human-ID gait database demonstrate that DGPP is superior the popular algorithms for dimension reduction, e.g., PCA, LDA, NPE and LPP.