Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Audio-Visual Emotion Recognition Using Gaussian Mixture Models for Face and Voice
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Audio–Visual Affective Expression Recognition Through Multistream Fused HMM
IEEE Transactions on Multimedia
Recognizing Human Emotional State From Audiovisual Signals*
IEEE Transactions on Multimedia
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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Effective analysis and recognition of human emotional behavior are important for achieving efficient and intelligent human computer interaction. This paper presents an approach for audiovisual based multimodal emotion recognition. The proposed solution integrates the audio and visual information by fusing the kernel matrices of respective channels through algebraic operations, followed by dimensionality reduction techniques to map the original disparate features to a nonlinearly transformed joint subspace. A hidden Markov model is employed for characterizing the statistical dependence across successive frames, and identifying the inherent temporal structure of the features. We examine the kernel fusion method at both feature and score levels. The effectiveness of the proposed method is demonstrated through extensive experimentation.