Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
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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
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised and unsupervised pattern recognition: feature extraction and computational intelligence
Supervised and unsupervised pattern recognition: feature extraction and computational intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
On the Stability of Source Separation Algorithms
Journal of VLSI Signal Processing Systems
A New Method of Feature Extraction and Its Stability
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Feature Extraction Based on ICA for Binary Classification Problems
IEEE Transactions on Knowledge and Data Engineering
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Feature Extraction by Sparse Coding and Independent Component Analysis
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Is ICA Significantly Better than PCA for Face Recognition?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Image covariance-based subspace method for face recognition
Pattern Recognition
Multi-class pattern classification based on a probabilistic model of combining binary classifiers
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Facial expression recognition using two-class discriminant features
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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This study investigates a new method of feature extraction for classification problems. The method is based on the independent component analysis (ICA). However, unlike the original ICA, one of the unsupervised learning methods, it is developed for classification problems by utilizing class information. The proposed method is an extension of our previous work on binary-class problems to multi-class problems. It treats the class labels as input features in order to produce two sets of new features: one that carries much information on the class labels and the other that is irrelevant to the class. The learning rule for this method is obtained using the stochastic gradient method to maximize the likelihood of the observed data. Among the new features, using only class-relevant ones, the dimension of the feature space can be greatly reduced in line with the principle of parsimony, resulting better generalization. This method was applied to recognize face identities and facial expressions using various databases such as the Yale, AT&T (former ORL), Color FERET face databases and so on. The performance of the proposed method was compared with those of conventional methods such as the principal component analysis (PCA), Fisher's linear discriminant (FLD), etc. The experimental results show that the proposed method performs well for face recognition problems.