Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Principal Component Analysis (GPCA)
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
A Component-based Framework for Face Detection and Identification
International Journal of Computer Vision
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Over the past several decades, pattern classification based on subspace methodology is one of the most attractive research topics in the field of computer vision. In this paper, a novel probabilistic linear subspace approach is proposed, which utilizes hybrid way to capture multidimensional data extracting maximum discriminative information and circumventing small eigenvalues by minimizing statistical dependence between components. During features extraction process, local region is emphasized for crucial patterns representation, and also statistic technique is used to regularize these unreliable information for both reducing computational cost and maintaining accuracy purposes. Our approach is validated with a high degree of accuracy with various face applications using challenging databases containing different variations.