Floating search methods in feature selection
Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
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ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
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Optimal sampling of Gabor features for face recognition
Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters
A multi-matcher for ear authentication
Pattern Recognition Letters
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Image and Vision Computing
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Fusion of color spaces for ear authentication
Pattern Recognition
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Pattern Recognition
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Pattern Recognition Letters
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International Journal of Biometrics
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International Journal of Information Technology and Management
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International Journal of Computer Applications in Technology
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We describe a Gabor feature selection technique that allows us to develop a fast and robust Gabor-feature based biometric system. Existing Gabor based methods use a huge number of Gabor features to represent the patterns. Our experiments on different biometric characteristics show that by using only few (∼10) Gabor features, it is possible to achieve a very low Equal Error Rate (EER). Only a low number of Gabor filters and sub-images, selected by running the Sequential Forward Floating Selection (SFFS), are exploited in the fusion step. The system has been tested on two biometric traits: ear authentication and finger authentication.