A novel bimodal emotion recognition approach from facial expression and speech based on
the sparse kernel reduced-rank regression (SKRRR) fusion method is proposed in this …

In this study, we propose a new method of recognizing emotional states from physiological
signals. Our proposal uses signal processing techniques to analyze physiological signals. It …

In this paper we present an effective framework for multimodal emotion recognition based on
a novel approach for automatic peak frame selection from audio-visual video sequences …

Emotions play a crucial role in human-computer interaction. They are generally expressed
and perceived through multiple modalities such as speech, facial expressions, physiological …

Over the last decade, automated analysis of human affective behavior has become an active
research area in computer science, psychology, neuroscience, and related fields. This study …

Due to the highly variant face geometry and appearances, Facial Expression Recognition
(FER) is still a challenging problem. CNN can characterize 2-D signals. Therefore, for …

This paper proposes an automatic system for facial expression recognition using a hybrid
approach in the feature extraction phase (appearance and geometric). Appearance features …

Emotions play a key role in human-computer interaction. They are generally expressed
through several ways (eg facial expressions, speech, body postures and gestures, etc). In …

In this work, a typical hierarchical Support Vector Machine (SVM) classifier structure with
three stages is adopted to identify 6 affective modes of happy, angry, excited, nervous, sad …

In this paper, we present a multimodal approach for the emotion recognition that takes into
account more sources of information (physiological signals, facial expressions, speech, etc) …