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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.13534 |
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| _version_ | 1866911213379649536 |
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| author | Geoffroy, Thibault Gerspacher, Gauthier Prevost, Lionel |
| author_facet | Geoffroy, Thibault Gerspacher, Gauthier Prevost, Lionel |
| contents | Incremental learning is a complex process due to potential catastrophic forgetting of old tasks when learning new ones. This is mainly due to transient features that do not fit from task to task. In this paper, we focus on complex emotion recognition. First, we learn basic emotions and then, incrementally, like humans, complex emotions. We show that Action Units, describing facial muscle movements, are non-transient, highly semantical features that outperform those extracted by both shallow and deep convolutional neural networks. Thanks to this ability, our approach achieves interesting results when learning incrementally complex, compound emotions with an accuracy of 0.75 on the CFEE dataset and can be favorably compared to state-of-the-art results. Moreover, it results in a lightweight model with a small memory footprint. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High Semantic Features for the Continual Learning of Complex Emotions: a Lightweight Solution Geoffroy, Thibault Gerspacher, Gauthier Prevost, Lionel Computer Vision and Pattern Recognition Incremental learning is a complex process due to potential catastrophic forgetting of old tasks when learning new ones. This is mainly due to transient features that do not fit from task to task. In this paper, we focus on complex emotion recognition. First, we learn basic emotions and then, incrementally, like humans, complex emotions. We show that Action Units, describing facial muscle movements, are non-transient, highly semantical features that outperform those extracted by both shallow and deep convolutional neural networks. Thanks to this ability, our approach achieves interesting results when learning incrementally complex, compound emotions with an accuracy of 0.75 on the CFEE dataset and can be favorably compared to state-of-the-art results. Moreover, it results in a lightweight model with a small memory footprint. |
| title | High Semantic Features for the Continual Learning of Complex Emotions: a Lightweight Solution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.13534 |