Multimodal Emotion Recognition in Response to Videos

By Mohammad Soleymani, Maja Pantic, Thierry Pun. IEEE Transactions on Affective Computing, 2012

697 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Pharmacy, Social Psychology

View Paper: https://www.semanticscholar.org/paper/e1cef5dbc358e57ea7d68dc49ca6cfe3a5225f6e

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In Experimental and Cognitive Psychology

In Pharmacy

In Social Psychology

Citations in the corpus

Speech emotion recognition using convolutional and Recurrent Neural Networks (2016, methodology): “Furthermore, the cross-corpus emotion recognition method was explored in [17], which investigates the ability of an emotion recognition detector to be applied to other databases.”
Emotions Recognition Using EEG Signals: A Survey (2019, methodology): “[71] Biosemi Active Two (256 Hz) IS (32)”
Physiological Signals-based Emotion Recognition via High-order Correlation Learning (2019, background): “It is universally acknowledged that the emotional response of human beings under external stimuli is often accompanied by the change of physiological features [36], such as brain activities, heart rate, muscle activities, blood pressure, skin temperature, and so on.”
EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “[203] studied the importance of diferent EEG features in emotion recognition.”
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, methodology): “[3] proposed a user-independent emotion recognition method using EEG, pupillary response and gaze distance, which achieved the best classification accuracies of 68.”
Multimodal Machine Learning: A Survey and Taxonomy (2017, background): “Multimodal fusion has a very broad range of applications, including audio-visual speech recognition (AVSR) [170], multimodal emotion recognition [200], medical image analysis [93], and multimedia event detection [122].”
Exploring EEG Features in Cross-Subject Emotion Recognition (2018, background): “Speciﬁcally, through evaluation over individual channels, we found that the channels with the best performances were mainly located in the bilateral temporal regions, which was consistent with the ﬁnding in Soleymani et al. (2012), Zheng et al. (2016).”
Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition (2021, background): “Therefore, recognizing emotions with multiple modalities has become a promising method [20], [21], [22], [23].”
EEG-based emotion classification using deep belief networks (2014, methodology): “In multimedia content analysis, affective characteristics of multimedia are important features for describing multimedia content [1].”
A Review of Human Activity Recognition Methods (2015, background): “(B) Hierarchical latent discriminative model proposed by Song et al. (2013).”
Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals (2018, methodology): “tion [8], [15], [16], [17], [29], [30], [31], we chose this system to collect EEG signals.”
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (2018, background): “Numerous previous approaches have relied on fusion techniques that leverage multiple modalities for affect recognition (Soleymani et al., 2012; Zadeh et al., 2017; Chen et al., 2017; Tzirakis et al., 2017; Zadeh et al., 2018b).”
Human Emotion Recognition: Review of Sensors and Methods (2020, methodology): “4 percent for three labels of arousal [188]”