Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Linear Dimensionality Reduction
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Face Recognition Using IPCA-ICA Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Constructing PCA Baseline Algorithms to Reevaluate ICA-Based Face-Recognition Performance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Shared Feature Extraction for Nearest Neighbor Face Recognition
IEEE Transactions on Neural Networks
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Analysis and classification of facial images have been a challenging topic in the field of pattern recognition and computer vision. In order to get efficient features from raw facial images, a large number of feature extraction methods have been developed. Still, the necessity of more sophisticated feature extraction method has been increasing as the classification purposes of facial images are diversified. In this paper, we propose a method for segregating facial image space into two subspaces according to a given purpose of classification. From raw input data, we first find a subspace representing noise features which should be removed for widening class discrepancy. By segregating the noise subspace, we can obtain a residual subspace which includes essential information for the given classification task. We then apply some conventional feature extraction method such as PCA and ICA to the residual subspace so as to obtain some efficient features. Through computational experiments on various facial image classification tasks - individual identification, pose detection, and expression recognition - , we confirm that the proposed method can find an optimized subspace and features for each specific classification task.