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Robust emotion recognition from low quality and low bit rate video: A deep learning approach
10 references·0 citing·45 second hop
Robust emotion recognition from low quality and low bit rate video: A deep learning approach (2017)Robust emotion recognition from low quali…How deep neural networks can improve emotion recognition on video data (2016)How deep neural networks can …AVEC 2016 (2016)AVEC 2016Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing (2015)Tackling Mental Health by Int…Recurrent Neural Networks for Emotion Recognition in Video (2015)Recurrent Neural Networks for…Long Short Term Memory Recurrent Neural Network based Multimodal Dimensional Emotion Recognition (2015)Long Short Term Memory Recurr…Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition? (2015)Do Deep Neural Networks Learn…Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions (2013)Introducing the RECOLA multim…Combining modality specific deep neural networks for emotion recognition in video (2013)Combining modality specific d…The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010)The Extended Cohn-Kanade Data…Evidence for a three-factor theory of emotions (1977)Evidence for a three-factor t…An approach to environmental psychology (1974)A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions (2008)The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2015)CHEAVD: a Chinese natural emotional audio–visual database (2016)Multi-Modal Audio, Video and Physiological Sensor Learning for Continuous Emotion Prediction (2016)The Measurement of Meaning (1957)Statistical principles in experimental design. (1962)Manual for the State-Trait Anxiety Inventory (1970)Toward a consensual structure of mood. (1985)Core affect and the psychological construction of emotion. (2003)A circumplex model of affect. (1980)Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research (1988)Measuring emotion: The self-assessment manikin and the semantic differential (1994)Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network (2016)End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017)Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers (2019)Collecting Large, Richly Annotated Facial-Expression Databases from Movies (2012)Going deeper in facial expression recognition using deep neural networks (2016)Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2020)Facial Action Coding System (1978)Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior (2005)Facial Expression Recognition using Convolutional Neural Networks: State of the Art (2016)Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition (2017)Deep Learning for Human Affect Recognition: Insights and New Developments (2019)OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit (2009)EmoNets: Multimodal deep learning approaches for emotion recognition in video (2015)Hierarchical committee of deep convolutional neural networks for robust facial expression recognition (2016)Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives (2020)Coefficient Alpha and the Internal Structure of Tests (1951)Intraclass correlations: Uses in assessing rater reliability. (1979)A Concordance Correlation Coefficient to Evaluate Reproducibility (1989)AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019)Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2019)Machine Learning in Mental Health (2020)Affective Computing: A Review (2005)Detecting depression from facial actions and vocal prosody (2009)Sentiment analysis and affective computing for depression monitoring (2017)Touchscreen typing pattern analysis for remote detection of the depressive tendency (2019)Inherent Tradeoffs in the Fair Determination of Risk Scores (2023)AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017)Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (2017)Deep Facial Expression Recognition: A Survey (2020)Audio-visual emotion recognition using deep transfer learning and multiple temporal models (2017)Deep convolutional BiLSTM fusion network for facial expression recognition (2019)Multi-Modal Recurrent Attention Networks for Facial Expression Recognition (2020)
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References

  • 2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression4,299 cited
  • 1977Evidence for a three-factor theory of emotions2,057 cited
  • 2013Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions776 cited
  • 2016AVEC 2016505 cited
  • 2013Combining modality specific deep neural networks for emotion recognition in video391 cited
  • 2015Recurrent Neural Networks for Emotion Recognition in Video386 cited
  • 2015Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?285 cited
  • 2015Long Short Term Memory Recurrent Neural Network based Multimodal Dimensional Emotion Recognition124 cited
  • 2016How deep neural networks can improve emotion recognition on video data107 cited
  • 2015Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing104 cited

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