Emotion Recognition with Facial Attention and Objective Activation Functions
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929555216793600 |
|---|---|
| author | Miskow, Andrzej Altahhan, Abdulrahman |
| author_facet | Miskow, Andrzej Altahhan, Abdulrahman |
| contents | In this paper, we study the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM, to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task. We show that not only attention can significantly improve the performance of these models but also that combining them with a different activation function can further help increase the performance of these models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emotion Recognition with Facial Attention and Objective Activation Functions Miskow, Andrzej Altahhan, Abdulrahman Computer Vision and Pattern Recognition Artificial Intelligence In this paper, we study the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM, to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task. We show that not only attention can significantly improve the performance of these models but also that combining them with a different activation function can further help increase the performance of these models. |
| title | Emotion Recognition with Facial Attention and Objective Activation Functions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2410.17740 |