A Multimodal Database for Affect Recognition and Implicit Tagging

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

1,559 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/3a60678ad2b862fa7c27b11f04c93c010cc6c430

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Citations in the corpus

MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception (2017, background): “This section overviews techniques to record emotional databases, emphasizing the role of recordings collected by actors.”
EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, methodology): “[3] estimated the logarithms spectral power in five EEG bands and concatenated them as features for emotional recognition with SVM as the classifier.”
Visual Heart Rate Estimation with Convolutional Neural Network (2018, methodology): “Authors tested the three methods on the MAHNOB HCITagging database [14].”
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018, background): “Examples are the SAL [21], SEMAINE [38], MAHNOB-HCI [52], Belfast naturalistic 2 , Belfast induced [51], DEAP [29], RECOLA [45], SEWA 3 and AFEW-VA [30] databases.”
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis (2016, background): “Many studies have theoretically and empirically demonstrated the advantage of integrating multiple modalities in human emotion perception relative to using a single modality [18, 24, 13, 23].”
DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices (2018, methodology): “2) ECG-based features: Many studies have shown that features extracted from ECG signals correlate with changes in the affective state of a person [1], [2], [12], with the most con-”
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, methodology): “Two datasets - OBF [11] and MAHNOB-HCI [23] are used in our experiments.”
TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, methodology): “To evaluate the proposed TSception, we conducted several experiments on two publicly available benchmark datasets, a Database for Emotion Analysis using Physiological signals (DEAP) 1 [33], and a multimodal database for affect recognition and implicit tagging (MAHNOB-HCI) 2 [37].”
AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups (2017, methodology): “Databases based on physiological signals include the MAHNOB-HCI [6] One database for personality research using video modality is the Mission Survival II corpus [13].”
Remote Heart Rate Measurement from Face Videos under Realistic Situations (2014, methodology): “In this paper, we propose a new framework for remote HR measurement which can work under challenging realistic HCI situations, and we evaluate it on a multi-modal database MAHNOB-HCI [17].”
A survey on empathetic dialogue systems (2020, background): “In the dimensional approach, emotions are represented as vectors denoting arousal and valence [67].”
Algorithmic Principles of Remote PPG (2017, methodology): “8The MAHNOB-HCI dataset created by [20] for affect recognition is unsuitable for the rPPG task, as the recorded videos are compressed in MPEG-4 format, i.”
Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection (2016, methodology): “method, hardware setup and the database, we refer the reader to the original database article [31].”
EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation (2014, background): “[6] provided a multimodal dataset, called “MAHNOB-HCI,” for an analysis of human affective states.”