Paluzo Hidalgo, EduardoGonzález Díaz, RocíoAguirre Carrazana, Guilermo2022-06-302022-06-302022Paluzo 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), 644-660.2688-1594https://hdl.handle.net/11441/134861The 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.application/pdf17engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Topological data analysisPersistent homologyPersistent entropyNeural networksAudio-visual emotion recognitionTalking-face videosinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.3934/era.2022034