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  1. The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. Visual emotion recognition has been extensively studied. Recently several end-to-end trained deep neural networks have been proposed for this task. However, such models often lack generalisation ability across datasets ...
  2. This paper proposes a general and independent deep facial expression feature extractor called Deep Facial Expression Vector ExtractoR (DeepFEVER). DeepFEVER is a convolu-tional neural network (CNN) trained using: (1) two labeled FER datasets, (2) an additional unlabeled dataset and (3) the technique known as knowledge distillation [4].
  3. researchgate.net

    PDF | On Sep 19, 2021, Liam Schoneveld and others published Towards a General Deep Feature Extractor for Facial Expression Recognition | Find, read and cite all the research you need on ResearchGate
  4. semanticscholar.org

    The Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset, is proposed. The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for ...
  5. paperswithcode.com

    However, such models often lack generalisation ability across datasets. In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset.
  6. However, such models often lack generalisation ability across datasets. In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset. DeepFEVER outperforms state-of ...
  7. emergentmind.com

    Towards a General Deep Feature Extractor for Facial Expression Recognition (2201.07781) Published ... Abstract. The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. ... Visual emotion recognition has been extensively studied.
  8. pubmed.ncbi.nlm.nih.gov

    Oct 25, 2024The suggested method tested Four different traditional supervised classifiers with deep features, Experimental found that AlexNet excels as a feature extractor, while SVM demonstrates superiority as a classifier because of this combination achieving the highest accuracy rates of 99.0% and 95.16% for the CK+ database and the JAFFE datasets ...
  9. researchgate.net

    In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any ...

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