Generative neural network for emotion recognition
Recognising affect from visual data has long been a research interest. However, annota- tions of affect in images/videos are expensive to acquire and current datasets all have limitations of either being too small or containing imbalanced affect classes. . In other at- tempts of data augmentation fo...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77200 |
| _version_ | 1811682654497538048 |
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| author | Chen, Hailin |
| author2 | Jagath C. Rajapakse |
| author_facet | Jagath C. Rajapakse Chen, Hailin |
| author_sort | Chen, Hailin |
| collection | NTU |
| description | Recognising affect from visual data has long been a research interest. However, annota- tions of affect in images/videos are expensive to acquire and current datasets all have limitations of either being too small or containing imbalanced affect classes. . In other at- tempts of data augmentation for emotion recognition, generation is all modelled as image translation task. In this paper, we first analyse generative models and multiple relevant GAN variants. We then propose to boost performance of emotion recognition model by investigating two generative models with one being image translation model using GANs and the other model to generate target data distribution with latent noise as input. In this way, we can achieve richer and more flexible data augmentation. Experiments on fer2013 dataset[11] showed effectiveness of our methods. |
| first_indexed | 2024-10-01T04:00:17Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77200 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:00:17Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/772002023-03-03T20:27:48Z Generative neural network for emotion recognition Chen, Hailin Jagath C. Rajapakse School of Computer Science and Engineering Bioinformatics Research Centre DRNTU::Engineering::Computer science and engineering Recognising affect from visual data has long been a research interest. However, annota- tions of affect in images/videos are expensive to acquire and current datasets all have limitations of either being too small or containing imbalanced affect classes. . In other at- tempts of data augmentation for emotion recognition, generation is all modelled as image translation task. In this paper, we first analyse generative models and multiple relevant GAN variants. We then propose to boost performance of emotion recognition model by investigating two generative models with one being image translation model using GANs and the other model to generate target data distribution with latent noise as input. In this way, we can achieve richer and more flexible data augmentation. Experiments on fer2013 dataset[11] showed effectiveness of our methods. Bachelor of Engineering (Computer Science) 2019-05-15T08:36:57Z 2019-05-15T08:36:57Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77200 en Nanyang Technological University 48 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Chen, Hailin Generative neural network for emotion recognition |
| title | Generative neural network for emotion recognition |
| title_full | Generative neural network for emotion recognition |
| title_fullStr | Generative neural network for emotion recognition |
| title_full_unstemmed | Generative neural network for emotion recognition |
| title_short | Generative neural network for emotion recognition |
| title_sort | generative neural network for emotion recognition |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/77200 |
| work_keys_str_mv | AT chenhailin generativeneuralnetworkforemotionrecognition |