Algorithms for clustering data
Algorithms for clustering data
Video and Image Semantics: Advanced Tools for Telecommunications
IEEE MultiMedia
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple matchers for a high security fingerprint verification system
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia Systems - Special section on video libraries
Fusion of appearance-based face recognition algorithms
Pattern Analysis & Applications
Combining classifiers for face recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Journal of Cognitive Neuroscience
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In this paper, we propose a novel approach for fusing two classifiers, specifically classifiers based on subspace analysis, during feature extraction. A method of combining the covariance matrices of the Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) is presented. Unlike other existing fusion strategies which fuse classifiers either at data level, or at feature level or at decision level, the proposed work combines two classifiers while extracting features introducing a new unexplored area for further research. The covariance matrices of PCA and FLD are combined using a product rule to preserve the natures of both covariance matrices with an expectation to have an increased performance. In order to show the effectiveness of the proposed fusion method, we have conducted a visual simulation on iris data. The proposed model has also been tested by performing clustering on standard datasets such as Zoo, Wine, and Iris. To study the versatility of the proposed method we have carried out an experimentation on sports video shot retrieval problem. The experimental results signify that the proposed fusing approach has an improved performance over individual classifiers.