Floating search methods in feature selection
Pattern Recognition Letters
Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
The nature of statistical learning theory
The nature of statistical learning theory
Color Based Object Recognition
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Log-Opponent Chromaticity Coding of Color Space
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Confidence based gating of colour features for face authentication
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Decision making in the LDA space: generalised gradient direction metric
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Bayesian Networks to Combine Intensity and Color Information in Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Face Verification Using Colour Kernels
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Face recognition using new image representations
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning-based image representation and method for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
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We consider the problem of fusing colour information to enhance the performance of a face authentication system. The discriminatory information potential of a vast range of colour spaces is investigated. The verification process is based on the normalised correlation in an LDA feature space. A sequential search approach which is in principle similar to the "plus L and take away R" algorithm is applied in order to find an optimum subset of the colour spaces. The colour based classifiers are combined using the SVM classifier. We show that by fusing colour information using the proposed method, the resulting decision making scheme considerably outperforms the intensity based verification system