Emotion recognition in talking-face videos using persistent entropy and neural networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866914711875878912 |
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| author | Paluzo-Hidalgo, Eduardo Aguirre-Carrazana, Guillermo Gonzalez-Diaz, Rocio |
| author_facet | Paluzo-Hidalgo, Eduardo Aguirre-Carrazana, Guillermo Gonzalez-Diaz, Rocio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_13571 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Emotion recognition in talking-face videos using persistent entropy and neural networks Paluzo-Hidalgo, Eduardo Aguirre-Carrazana, Guillermo Gonzalez-Diaz, Rocio Computer Vision and Pattern Recognition Algebraic Topology 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. |
| title | Emotion recognition in talking-face videos using persistent entropy and neural networks |
| topic | Computer Vision and Pattern Recognition Algebraic Topology |
| url | https://arxiv.org/abs/2110.13571 |