… emotion recognition method based on weighted feature facial expression is presented. It is
motivated by the fact that emotion … has different impact on recognition results. Different from …

… emotion recognition, this paper proposed a novel speech emotion recognition approach based
on Gaussian Kernel … (PSVM) to recognize four basic human emotions (angry, joy, sadness…

… a better emotion recognition system. For every instance, the emotion score ranging from
0 to 100 is given for all the six emotions (Anger, Disgust, Fear, Joy, Sadness and Surprise). …

… As in the case of Gaussian Process regression, kernel function parameters can be learned
by marginal likelihood p(y|X, θ) maximization. However, in this case, the likelihood p(y|f) is no …

… ance matrix, and Gaussian distribution) respectively, which can all be viewed as points
residing on some Riemannian manifolds. Then different Riemanian kernels are employed on …

… novel speech emotion classification approach based on the multiple kernel Gaussian process…
Rigoll, “Speaker independent emotion recognition by early fusion of acoustic and linguistic …

… In this work, we investigate the way that emotional … emotion recognition accuracy. In
particular, the behaviors of different facial regions are studied in detail. We analyze an emotion …

… , the topic of EEG-based emotion recognition is still a challenging problem. This paper
introduced LR with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The …

… In this paper, an approach for emotion recognition based on physiological signals is … rates
compared to the Gaussian kernel. Similarly, using the linear kernel, we obtain recognition …

… We propose a bagged ensemble comprising of support vector machines with a Gaussian
kernel as a viable algorithm for the problem at hand. We report the results obtained on the …