… important for emotion recognition. However, … network framework called spatial–temporal
RNN (STRNN) to deal with both EEG-based emotion recognition and facial emotion recognition. …

… Given that the primary focus of this work is on vision based emotion recognition, we simply
used … It uses 1582 features extracted with the open-source Emotion and Affect Recognition (…

… to utilize deep neural networks (DNNs) to extract high level features from raw data and
show that they are effective for speech emotion recognition. We first produce an emotion state …

… In this paper, we present a language-independent emotion recognition system for the
identification of human affective state in the speech signal. A corpus of emotional speech from …

… In this paper, we present a fully deep neural network model for facial emotion recognition
and the model has been tested on two public datasets to assess the performance of the …

… attention to the recognition of nonverbal information, and have especially focused on emotion
recognition. Many kinds of physiological characteristics are used to extract emotions, such …

… approaches cannot encode and learn both semantic and emotional relationship in short
text … posed a novel neural network architecture, called semanticemotion neural network (SENN) …

… Our best single model was a convolutional neural network trained to predict emotions from
static frames using two large data sets, the Toronto Face Database and our own set of faces …

… recognizing emotions. In this paper, we present a new model for continuous emotion recognition
… -to-end, is comprised of a Convolutional Neural Network (CNN), which extracts features …

… In recent years, deep neural networks have been used with great success in determining
emotional states. Inspired by this success, we propose an emotion recognition system using …