Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Central Subspace Dimensionality Reduction Using Covariance Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Affective Computing
Modeling latent discriminative dynamic of multi-dimensional affective signals
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
IEEE Transactions on Affective Computing
AVEC 2012: the continuous audio/visual emotion challenge - an introduction
Proceedings of the 14th ACM international conference on Multimodal interaction
Image and Vision Computing
Hierarchical On-line Appearance-Based Tracking for 3D head pose, eyebrows, lips, eyelids and irises
Image and Vision Computing
| Hi-index | 0.00 |
Adopting continuous dimensional annotations for affective analysis has been gaining rising attention by researchers over the past years. Due to the idiosyncratic nature of this problem, many subproblems have been identified, spanning from the fusion of multiple continuous annotations to exploiting output-correlations amongst emotion dimensions. In this paper, we firstly empirically answer several important questions which have found partial or no answer at all so far in related literature. In more detail, we study the correlation of each emotion dimension (i) with respect to other emotion dimensions, (ii) to basic emotions (e.g., happiness, anger). As a measure for comparison, we use video and audio features. Interestingly enough, we find that (i) each emotion dimension is more correlated with other emotion dimensions rather than with face and audio features, and similarly (ii) that each basic emotion is more correlated with emotion dimensions than with audio and video features. A similar conclusion holds for discrete emotions which are found to be highly correlated to emotion dimensions as compared to audio and/or video features. Motivated by these findings, we present a novel regression algorithm (Correlated-Spaces Regression, CSR), inspired by Canonical Correlation Analysis (CCA) which learns output-correlations and performs supervised dimensionality reduction and multimodal fusion by (i) projecting features extracted from all modalities and labels onto a common space where their inter-correlation is maximised and (ii) learning mappings from the projected feature space onto the projected, uncorrelated label space.