  Emotion recognition has become an important field of research in the
human-computer interactions domain. The latest advancements in the field show
that combining visual with audio information lead to better results if compared
to the case of using a single source of information separately. From a visual
point of view, a human emotion can be recognized by analyzing the facial
expression of the person. More precisely, the human emotion can be described
through a combination of several Facial Action Units. In this paper, we propose
a system that is able to recognize emotions with a high accuracy rate and in
real time, based on deep Convolutional Neural Networks. In order to increase
the accuracy of the recognition system, we analyze also the speech data and
fuse the information coming from both sources, i.e., visual and audio.
Experimental results show the effectiveness of the proposed scheme for emotion
recognition and the importance of combining visual with audio data.
