The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Contourlet-Based Feature Extraction with PCA for Face Recognition
AHS '08 Proceedings of the 2008 NASA/ESA Conference on Adaptive Hardware and Systems
Steerable pyramid-based face hallucination
Pattern Recognition
Comparison and fusion of multiresolution features for texture classification
Pattern Recognition Letters
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Rotation-invariant texture retrieval with gaussianized steerable pyramids
IEEE Transactions on Image Processing
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
Face recognition by curvelet based feature extraction
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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In this paper, an efficient local appearance feature extraction method based steerable pyramid (S-P) is proposed for face recognition. Local information is extracted from S-P sub-bands using block-based statistics. The underlying statistics allow us to reduce the required amount of data to be stored. The obtained local features are combined at the feature and decision level to enhance face recognition performance. Experimental results on ORL, Yale and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.