The HUMAINE Database: Addressing the Collection and Annotation of Naturalistic and Induced Emotional Data

By Ellen Douglas-Cowie, R. Cowie, Ian Sneddon, Cate Cox, Orla Lowry, Margaret McRorie, Jean-Claude Martin, Laurence Y. Devillers, Sarkis Abrilian, Anton Batliner and 2 more. Affective Computing and Intelligent Interaction, 2007

366 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/2d6e537b4eda87c5acb1678aba83a2339e5433db

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Citations in the corpus

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “The Green Persuasive Dataset (Douglas-Cowie et al., 2007) was recorded in 2007 to provide data for the HUMAINE project, whose goal is to develop interfaces that can register and respond to emotion.”
Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies (2008, methodology): “As database we use the induced Belfast Sensitive Artificial Listener data which is part of the final HUMAINE database [3].”
Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions (2013, methodology): “The latest and most extensive overview of emotional corpora can be found in the HUMAINE database [5], whereas those devoted to others social behaviors are reviewed in [6].”
ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors (2018, background): “HUMAINE [13] var var audio, visual, physiological yes no includes six sub-collections (some non-public) DEAP [10] 32 40 physiological yes no focus on music videos DECAF [9] 30 76 face, physiological yes no compares music and movie clips MAHNOB-HCI [11] 27 20 face, audio, eye gaze, physiological yes ”
Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals (2014, neutral): “, 2005), HUMAINE (Douglas-Cowie et al., 2007), VAM (Grimm et al.”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “…from weak signals embedded in noisy channels; (c) difﬁculties associated with collecting adequate realistic training data for machine learning [Douglas-Cowie et al. 2007]; et al. 2006]; (e) challenges of incorporating top-down models of context with bottom-up body-based sensing [Conati and…”
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database (2014, background): “, [1,2,7,8,16]), as well as some with 3D imaging data (e.”
CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset (2014, methodology): “Similarly, the HUMAINE [39] and RECOLA [40] databases have many different types of labels to get at different aspects of audio-visual emotional expression, but all annotations are made for portions of the recording where both vocal and facial information is present.”
Induced Disgust , Happiness and Surprise : an Addition to the MMI Facial Expression Database (2010, methodology): “979 Hz 48kHz Scientific Yes Yes Spaghetti db (Douglas-Cowie et al., 2007) 3 1:35 352x288 pixels @ 25Hz Unknown Scientific Yes No Vera am Mittag db (Grimm et al.”
A Multimodal Database for Affect Recognition and Implicit Tagging (2012, background): “These databases mostly include speech, visual, or audio-visual data [5], [6], [7], [8].”
A review of affective computing: From unimodal analysis to multimodal fusion (2017, neutral): “[28] A + V 50 Emotion words, authenticity,”
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 (2012, methodology): “2003; Fernandez and Picard 2003). In contrast, Support Vector Machines (SVMs) have been used more recently and seem to be promising as a classification schema for emotion recognition in speech, as assessed in many papers (Schuller et al. 2005a,c, 2009c, 2010; Wang et al. 2008; Graciarena et al. 2006”
The Vera am Mittag German audio-visual emotional speech database (2008, background): “Although in recent years much effort has been invested in collecting and segmenting audio-visual speech data [1, 2, 3, 4], there is still a lack of spontaneous and emotionally rich speech corpora.”