Correction: Validation of the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV): A Set of Videos Expressing Low, Intermediate, and High Intensity Emotions

Wingenbach, Tanja S. H.; Ashwin, Chris; Brosnan, Mark

doi:10.1371/journal.pone.0168891

Cited by 11 publications

(21 citation statements)

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“…Accordingly, we computed the objective evidence of each of six basic expressions and also the evidence of each of 20 AUs, across 31 intensities from neutral (0% intensity) to emotional (100% intensity) in 3.33% intensity steps. This adds to recent work ( Calvo et al, 2016 ; Wingenbach et al, 2016 ) regarding the role of intensity on the categorization of dynamic expressions. This approach will be particularly useful for expression discrimination studies, e.g., the lowest intensity or threshold at which a particular emotion is recognized and differentiated from others and from neutral faces.…”

Section: Introductionmentioning

confidence: 58%

“…Video-clips are shown as supporting information (see Supplementary Dataset S4 ). A very similar or identical procedure and display duration was used previously ( Schultz and Pilz, 2009 ; Johnston et al, 2013 ; Wingenbach et al, 2016 ). Each face stimulus subtended a visual angle of 10.6° (height) × 8° (width) at a 70-cm viewing distance (this approximates the size of a real face, i.e., 18.5 × 13.8 cm, from a 1-m distance).…”

Section: Methodsmentioning

confidence: 99%

“…Yet, in social encounters and face-to-face communication, facial expressions are generally dynamic. Further, research has shown that motion benefits affect recognition (see Krumhuber et al, 2013 ; Calvo et al, 2016 ; Wingenbach et al, 2016 ). Accordingly, it is important to use dynamic stimuli for investigating recognition of facial expressions.…”

Section: Introductionmentioning

confidence: 99%

“…A number of dynamic expression databases have been developed, generally involving on-line video recordings of facial activity, which represent a valuable advance (e.g., van der Schalk et al, 2011 ; Banziger et al, 2012 ; Kaulard et al, 2012 ; Zhang et al, 2014 ; O’Reilly et al, 2016 ; Wingenbach et al, 2016 ). Krumhuber et al (2017) have reviewed and discussed the major issues of 22 dynamic expression databases.…”

Section: Introductionmentioning

confidence: 99%

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Human Observers and Automated Assessment of Dynamic Emotional Facial Expressions: KDEF-dyn Database Validation

et al. 2018

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Most experimental studies of facial expression processing have used static stimuli (photographs), yet facial expressions in daily life are generally dynamic. In its original photographic format, the Karolinska Directed Emotional Faces (KDEF) has been frequently utilized. In the current study, we validate a dynamic version of this database, the KDEF-dyn. To this end, we applied animation between neutral and emotional expressions (happy, sad, angry, fearful, disgusted, and surprised; 1,033-ms unfolding) to 40 KDEF models, with morphing software. Ninety-six human observers categorized the expressions of the resulting 240 video-clip stimuli, and automated face analysis assessed the evidence for 6 expressions and 20 facial action units (AUs) at 31 intensities. Low-level image properties (luminance, signal-to-noise ratio, etc.) and other purely perceptual factors (e.g., size, unfolding speed) were controlled. Human recognition performance (accuracy, efficiency, and confusions) patterns were consistent with prior research using static and other dynamic expressions. Automated assessment of expressions and AUs was sensitive to intensity manipulations. Significant correlations emerged between human observers’ categorization and automated classification. The KDEF-dyn database aims to provide a balance between experimental control and ecological validity for research on emotional facial expression processing. The stimuli and the validation data are available to the scientific community.

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Section: Introductionmentioning

confidence: 58%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human Observers and Automated Assessment of Dynamic Emotional Facial Expressions: KDEF-dyn Database Validation

et al. 2018

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“…contempt, pride and embarrassment) besides neutral emotion. Wingenbach et al [20] created this dataset by editing 120 videos played by 12 North European actors (5 females, 7 males) to add three levels of intensity. They created three new videos displaying the same emotion at three different degrees of intensity: low, medium and high, for a total of 360 videos.…”

Section: Dataset Usedmentioning

confidence: 99%

Emotion Recognition System Based on Hybrid Techniques

Abdulsalam¹,

Alhamdani²,

Abdullah³

2019

IJMLC

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Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions.The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication.For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier.In addition, a bi-modal system for recognising emotions from facial expressions and speech signals is presented. This is important since one modality may not provide sufficient information or may not be available for any reason beyond operator control. To perform this, decision-level fusion is performed using a novel way for weighting according to the proportions of facial and speech impressions. The results show an average accuracy of 93.22 %.

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Affective Computing for Visual Emotion Recognition Using Convolutional Neural Networks

Ashraf

Gunawan

Rahman

et al. 2021

Advances in Robotics, Automation and Data Analytics

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Affective computing is a developing interdisciplinary examination field uniting specialists and experts from different fields, from artificial intelligence, natural language processing to intellectual and sociologies. The thought behind affective computing is to give computers the aptitude of insight that will, in general, comprehend human feelings. Notwithstanding these victories, the field needs hypothetical firm establishments and efficient rules in numerous regions, especially in feeling demonstrating and developing computational models of feeling. This exploration manages affective computing to improve the exhibition of Human-Machine Interaction. This work's focal point is to distinguish the emotional state of a human utilizing deep learning procedure, i.e., Convolutional Neural Networks (CNN) containing parameters like three convolution layers, pooling layers, learning rates, two fully connected layers, batch normalizations, and dropout ratios. The Warsaw Set of Emotional Facial Expression ▻ Authors ▻ Keywords Brought to you by INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA Search Sources Lists ↗ SciVal Create account Sign in References (14) Engineering uncontrolled terms Computational model Emotion recognition Human machine interaction Learning procedures NAtural language processing Performance parameters Recognition accuracy Training and testing Engineering main heading: Convolutional neural networks Funding details Funding sponsor Funding number Acronym

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Correction: Validation of the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV): A Set of Videos Expressing Low, Intermediate, and High Intensity Emotions

Cited by 11 publications

References 1 publication

Human Observers and Automated Assessment of Dynamic Emotional Facial Expressions: KDEF-dyn Database Validation

Human Observers and Automated Assessment of Dynamic Emotional Facial Expressions: KDEF-dyn Database Validation

Emotion Recognition System Based on Hybrid Techniques

Affective Computing for Visual Emotion Recognition Using Convolutional Neural Networks

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