Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space

By Mihalis A. Nicolaou, Hatice Gunes, Maja Pantic. IEEE Transactions on Affective Computing, 2011

505 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/b34c6081aac87d4d9f659c8f342c05ce7b835bba

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Citations in the corpus

Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection (2016, methodology): “For a detailed explanation of the synchronization method, hardware setup and the database, we refer the reader to the original database article [31].”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “, [Karpouzis et al. 2007; Nicolaou et al. 2011]) were categorized as “induced” because it is likely that the affective disposition of the agent induced specific emotions in the individual.”
A Review of Human Activity Recognition Methods (2015, methodology): “Nicolaou et al. (2011) proposed a regression model based on SVMs for regression (SVR) (Smola and Schölkopf, 2004) Frontiers in Robotics and AI | www.frontiersin.org November 2015 | Volume 2 | Article 2813 for continuous prediction of multimodal emotional states, using facial expression, shoulder…”
DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices (2018, methodology): “[19] proposed the use of features based on facial expression, shoulder gesture and audio cues along with Support Vector Machines for Regression (SVRs) and Bidirectional Long Short-Term Memory Neural Networks (BLSTM-NNs) for classification.”
AffectAura: an intelligent system for emotional memory (2012, background): “Over the past 15 years, there have been significant advances in the domain of affect recognition from multiple modalities [25].”
Static and dynamic 3D facial expression recognition: A comprehensive survey (2012, unknown): “prototypical expressions of emotions [5,4,6,7].”
IExpressNet: Facial Expression Recognition with Incremental Classes (2020, background): “There are two models [43, 80, 81] frequently applied to represent emotions: categorical emotion states (CES) [32, 37, 78] and dimensional emotion space (DES) [10, 44, 49] respectively.”
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, methodology): “Pearsons correlation coefficient is therefore proposed in some literature [24], [36], [37] to overcome this limitation”
Context-Aware Emotion Recognition Networks (2019, methodology): “[21] used the location of shoulders and Schindler et al.”
Categorical and dimensional affect analysis in continuous input: Current trends and future directions (2013, background): “, the valence dimension, as being positive (+) or negative (−), and the arousal dimension, as being active (+) or passive (−) [36].”
Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals (2014, background): “According to the dimensional approach, affective states are not independent from one another; rather, they are related to one another in a systematic manner (Mihalis et al., 2011).”