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arXiv · 2110.13571

Emotion recognition in talking-face videos using persistent entropy and neural networks

Eduardo Paluzo-Hidalgo·Guillermo Aguirre-Carrazana·Rocio Gonzalez-Diaz
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Abstract

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.

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Eduardo Paluzo-Hidalgo, Guillermo Aguirre-Carrazana, Rocio Gonzalez-Diaz. 2021-10-26. Emotion recognition in talking-face videos using persistent entropy and neural networks. https://doi.org/10.3934/era.2022034

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