Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Real time facial expression recognition in video using support vector machines
Proceedings of the 5th international conference on Multimodal interfaces
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal Processing
Automatic facial expression recognition using facial animation parameters and multistream HMMs
IEEE Transactions on Information Forensics and Security
Recognition of facial expressions and measurement of levels of interest from video
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Weighted Mahalanobis Distance Kernels for Support Vector Machines
IEEE Transactions on Neural Networks
Multiclass Posterior Probability Support Vector Machines
IEEE Transactions on Neural Networks
Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally
IEEE Transactions on Neural Networks
Improved natural language learning via variance-regularization support vector machines
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Expert Systems with Applications: An International Journal
Research of robust facial expression recognition under facial occlusion condition
AMT'11 Proceedings of the 7th international conference on Active media technology
Matrix pattern based minimum within-class scatter support vector machines
Applied Soft Computing
Expert Systems with Applications: An International Journal
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In this paper, a novel class of multiclass classifiers inspired by the optimization of Fisher discriminant ratio and the support vector machine (SVM) formulation is introduced. The optimization problem of the so-called minimum within-class variance multiclass classifiers (MWCVMC) is formulated and solved in arbitrary Hilbert spaces, defined by Mercer's kernels, in order to find multiclass decision hyperplanes/surfaces. Afterwards, MWCVMCs are solved using indefinite kernels and dissimilarity measures via pseudo-Euclidean embedding. The power of the proposed approach is first demonstrated in the facial expression recognition of the seven basic facial expressions (i.e), anger, disgust, fear, happiness, sadness, and surprise plus the neutral state) problem in the presence of partial facial occlusion by using a pseudo-Euclidean embedding of Hausdorff distances and the MWCVMC. The experiments indicated a recognition accuracy rate achieved up to 99%. The MWCVMC classifiers are also applied to face recognition and other classification problems using Mercer's kernels.