Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Distance measures for PCA-based face recognition
Pattern Recognition Letters
Feature-Based Affine-Invariant Localization of Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Confidence based gating of multiple face authentication experts
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Fusion of PCA-based and LDA-based similarity measures for face verification
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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In this paper, we address the problem of selecting and fusing similarity measures based classifiers in LDA face space. The performance of a face verification system in an LDA feature space using different similarity measure based classifiers is experimentally studied first. The study is performed for both manually and automatically registered face images. A sequential search approach which is in principle similar to the "plus L and take away R" algorithm is then applied in order to find an optimum subset of the adopted classifiers. The selected classifiers are combined using the SVM classifier. We show that although, individually, one of the adopted scoring functions, the Gradient Direction distance outperforms the other metrics, by fusing different similarity measures using the proposed method, the resulting decision making scheme improves the performance of the system in different conditions.