Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916491257970688 |
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| author | Roy, Shuvendu Etemad, Ali |
| author_facet | Roy, Shuvendu Etemad, Ali |
| contents | Deep learning-based methods have been the key driving force behind much of the recent success of facial expression recognition (FER) systems. However, the need for large amounts of labelled data remains a challenge. Semi-supervised learning offers a way to overcome this limitation, allowing models to learn from a small amount of labelled data along with a large unlabelled dataset. While semi-supervised learning has shown promise in FER, most current methods from general computer vision literature have not been explored in the context of FER. In this work, we present a comprehensive study on 11 of the most recent semi-supervised methods, in the context of FER, namely Pi-model, Pseudo-label, Mean Teacher, VAT, UDA, MixMatch, ReMixMatch, FlexMatch, CoMatch, and CCSSL. Our investigation covers semi-supervised learning from in-distribution, out-of-distribution, unconstrained, and very small unlabelled data. Our evaluation includes five FER datasets plus one large face dataset for unconstrained learning. Our results demonstrate that FixMatch consistently achieves better performance on in-distribution unlabelled data, while ReMixMatch stands out among all methods for out-of-distribution, unconstrained, and scarce unlabelled data scenarios. Another significant observation is that with an equal number of labelled samples, semi-supervised learning delivers a considerable improvement over supervised learning, regardless of whether the unlabelled data is in-distribution, out-of-distribution, or unconstrained. We also conduct sensitivity analyses on critical hyper-parameters for the two best methods of each setting. To facilitate reproducibility and further development, we make our code publicly available at: github.com/ShuvenduRoy/SSL_FER_OOD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_01229 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data Roy, Shuvendu Etemad, Ali Computer Vision and Pattern Recognition Deep learning-based methods have been the key driving force behind much of the recent success of facial expression recognition (FER) systems. However, the need for large amounts of labelled data remains a challenge. Semi-supervised learning offers a way to overcome this limitation, allowing models to learn from a small amount of labelled data along with a large unlabelled dataset. While semi-supervised learning has shown promise in FER, most current methods from general computer vision literature have not been explored in the context of FER. In this work, we present a comprehensive study on 11 of the most recent semi-supervised methods, in the context of FER, namely Pi-model, Pseudo-label, Mean Teacher, VAT, UDA, MixMatch, ReMixMatch, FlexMatch, CoMatch, and CCSSL. Our investigation covers semi-supervised learning from in-distribution, out-of-distribution, unconstrained, and very small unlabelled data. Our evaluation includes five FER datasets plus one large face dataset for unconstrained learning. Our results demonstrate that FixMatch consistently achieves better performance on in-distribution unlabelled data, while ReMixMatch stands out among all methods for out-of-distribution, unconstrained, and scarce unlabelled data scenarios. Another significant observation is that with an equal number of labelled samples, semi-supervised learning delivers a considerable improvement over supervised learning, regardless of whether the unlabelled data is in-distribution, out-of-distribution, or unconstrained. We also conduct sensitivity analyses on critical hyper-parameters for the two best methods of each setting. To facilitate reproducibility and further development, we make our code publicly available at: github.com/ShuvenduRoy/SSL_FER_OOD. |
| title | Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.01229 |