The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Subspace Analysis for Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Discriminant Analysis: Feature Extraction with an Information-Theoretic Objective
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Classifiability-Based Discriminatory Projection Pursuit
IEEE Transactions on Neural Networks - Part 1
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The heteroscedasticity problem is a great challenge in pattern recognition, particularly in statistics-based methods. The traditional method that is mainly used to solve this problem is heteroscedastic Discriminant Analysis. In this study, we propose a novel solution to the problem, called Super-class Discriminant Analysis (SCDA). Our method uses the ''divide and conquer'' methodology to partition the heteroscedastic dataset into super-classes with reduced heteroscedasticity and models them separately. Theoretically, a super-class should contain a set of classes having the same within-class variation. In practice, a heteroscedastic dataset can be coarsely divided into several super-classes based on certain semantic criteria such as gender or race. We evaluate our method with toy data and three real-world datasets, which can be divided into super-classes according to gender and race. Experimental results indicate that the proposed method can effectively resolve the problem of heteroscedasticity.