Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
Relational discriminant analysis
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Eigenspace-based face recognition: a comparative study of different approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A Study on Representations for Face Recognition from Thermal Images
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Optimizing dissimilarity-based classifiers using a newly modified hausdorff distance
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
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For high-dimensional classification tasks such as face recognition, the number of samples is smaller than the dimensionality of the samples. In such cases, a problem encountered in Linear Discriminant Analysis-based (LDA) methods for dimension reduction is what is known as the Small Sample Size (SSS) problem. Recently, a number of approaches that attempt to solve the SSS problem have been proposed in the literature. In this paper, a different way of solving the SSS problem compared to these is proposed. It is one that employs a dissimilarity representation method where an object is represented based on the dissimilarity measures among representatives extracted from training samples instead of from the feature vector itself. Thus, by appropriately selecting representatives and by defining the dissimilarity measure, it is possible to reduce the dimensionality and achieve a better classification performance in terms of both speed and accuracy. Apart from utilizing the dissimilarity representation, in this paper simultaneously employing a fusion technique is also proposed in order to increase the classification accuracy. The rationale for this is explained in the paper. The proposed scheme is completely different from the conventional ones in terms of the computation of the transformation matrix, as well as in controlling the number of dimensions. The present experimental results, which to the best of the authors' knowledge, are the first such reported results, demonstrate that the proposed mechanism achieves nearly identical efficiency results in terms of the classification accuracy compared with the conventional LDA-extension approaches for well-known face databases involving AT&T and Yale databases.