Article
Emotion recognition in talking-face videos using persistent entropy and neural networks
Author/s | Paluzo Hidalgo, Eduardo
González Díaz, Rocío Aguirre Carrazana, Guilermo |
Department | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) |
Publication Date | 2022 |
Deposit Date | 2022-06-30 |
Published in |
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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 vi sion, or psychology, ... 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 vi sion, 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 com pute a topology signature (a 9-dimensional vector) for each video. We prove that small changes in the video produce small changes in the signature, ensuring the stability of the method. These topological signatures are used to feed a neural network to distinguish between the following emotions: calm, happy, sad, angry, fearful, disgust, and surprised. The results reached are promising and competitive, beating the performances achieved in other state-of-the-art works found in the literature. |
Funding agencies | Agencia Estatal de Investigación. España Agencia Andaluza del Conocimiento |
Project ID. | PID2019-107339GB-100
P20-01145 |
Citation | Paluzo Hidalgo, E., González Díaz, R. y Aguirre Carrazana, G. (2022). Emotion recognition in talking-face videos using persistent entropy and neural networks. Electronic Research Archive, 30 (2), 644-660. |
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10.3934_era.2022034.pdf | 12.18Mb | [PDF] | View/ | |