  The automatic recognition of a person's emotional state has become a very
active research field that involves scientists specialized in different areas
such as artificial intelligence, computer vision or psychology, among others.
Our main objective in this work is to develop a novel approach, using
persistent entropy and neural networks as main tools, to recognise and classify
emotions from talking-face videos. Specifically, we combine audio-signal and
image-sequence information to compute a topology signature(a 9-dimensional
vector) for each video. We prove that small changes in the video produce small
changes in the signature. These topological signatures are used to feed a
neural network to distinguish between the following emotions: neutral, calm,
happy, sad, angry, fearful, disgust, and surprised. The results reached are
promising and competitive, beating the performance reached in other
state-of-the-art works found in the literature.
