A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A fast fixed-point algorithm for independent component analysis
Neural Computation
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Facial expression recognition using fisher weight maps
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Independent component analysis in a facial local residue space
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
Integrated Computer-Aided Engineering
Human facial expression recognition using hybrid network of PCA and RBFN
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Independent Component Analysis (ICA) is used widely to extract statistical independent features for analysis and discrimination in recent years. But its random properties make it very difficult to test the efficiency and validation of the extracted independent features. In this paper, we propose a new method called BoostedICA to solve such problems by running ICA several times and boosting the selected independent components. Because of the local extremum question in calculating independent component, several times of running could get the more valid components with larger probability. The AdaBoost algorithm can guarantee the discriminating efficient of the selected features from the statistical theory. The proposed method achieves both computational efficiency and accuracy through optimizing extracting and choosing features. Finally we describe face expression recognition experiments on person-dependent and person-independent. The experimental results of 97.5% and 86% recognition rate respectively show that our method has better performance compared with other methods.