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
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
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
On optimizing dissimilarity-based classification using prototype reduction schemes
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Eigenspace-based face recognition: a comparative study of different approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Dynamic Programming Technique for Optimizing Dissimilarity-Based Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A multiple combining method for optimizing dissimilarity-based classification
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Classification of three-way data by the dissimilarity representation
Signal Processing
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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. A number of LDA-extension approaches that attempt to solve the SSS problem have been proposed in the literature. Recently, a different way of employing a dissimilarity representation method was proposed [18], where an object was represented based on the dissimilarity measures among representatives extracted from training samples instead of the feature vector itself. Apart from utilizing the dissimilarity representation, in this paper, a new way of employing a fusion technique in representing features as well as in designing classifiers is proposed in order to increase the classification accuracy. The proposed scheme is completely different from the conventional ones in terms of the computation of the transformation matrix as well as the selection of the number of dimensions. The present experimental results demonstrate that the proposed combining mechanism works well and achieves further improved efficiency compared with the LDA-extension approaches for well-known face databases involving AT&T and Yale databases. The results especially demonstrate that the highest accuracy rates are achieved when the combined representation is classified with the trained combiners.