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Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression
0 references·8 citing·29 second hop
Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression (2014)Fusion of Audio-visual Features using Hie…Two-Stage Temporal Modelling Framework for Video-Based Depression Recognition Using Graph Representation (2024)Two-Stage Temporal Modelling …A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video (2024)A Multimodal Approach for Det…Integrating Deep Facial Priors Into Landmarks for Privacy Preserving Multimodal Depression Recognition (2023)Integrating Deep Facial Prior…Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition (2019)Multimodal Fusion with Deep N…ISLA: Temporal Segmentation and Labeling for Audio-Visual Emotion Recognition (2017)ISLA: Temporal Segmentation a…Bio-Visual Fusion for Person-Independent Recognition of Pain Intensity (2015)Bio-Visual Fusion for Person-…Audio-visual emotion recognition using multi-directional regression and Ridgelet transform (2015)Audio-visual emotion recognit…Inferring Depression and Affect from Application Dependent Meta Knowledge (2014)Inferring Depression and Affe…A circumplex model of affect. (1980)Affective Computing (1997)DEAP: A Database for Emotion Analysis ;Using Physiological Signals (2011)Comprehensive database for facial expression analysis (2002)A database of German emotional speech (2005)Web-Based Database for Facial Expression Analysis (2005)A Multimodal Facial Emotion Recognition Framework through the Fusion of Speech with Visible and Infrared Images (2020)Facial emotion recognition using temporal relational network: an application to E-learning (2020)Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN (2022)A Concordance Correlation Coefficient to Evaluate Reproducibility (1989)IEMOCAP: interactive emotional dyadic motion capture database (2008)Two-Stage Fuzzy Fusion Based-Convolution Neural Network for Dynamic Emotion Recognition (2020)MAGDRA: A Multi-modal Attention Graph Network with Dynamic Routing-By-Agreement for multi-label emotion recognition (2023)Acoustical properties of speech as indicators of depression and suicidal risk (2000)Detecting depression from facial actions and vocal prosody (2009)Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network (2016)Diagnostic and Statistical Manual of Mental Disorders(DSM) (2008)The Distress Analysis Interview Corpus of human and computer interviews (2014)Deep Facial Expression Recognition: A Survey (2020)Comparison of Beck Depression Inventories-IA and-II in Psychiatric Outpatients (1996)The PHQ-8 as a measure of current depression in the general population (2008)AVEC 2013 (2013)Emotion recognition in human-computer interaction (2001)HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case (2019)Painful data: The UNBC-McMaster shoulder pain expression archive database (2011)Automatic detection of pain intensity (2012)The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system (2013)Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals (2019)Using AI to Detect Pain through Facial Expressions: A Review (2023)
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Cited by

  • 2015Audio-visual emotion recognition using multi-directional regression and Ridgelet transform68 cited
  • 2015Bio-Visual Fusion for Person-Independent Recognition of Pain Intensity63 cited
  • 2023Integrating Deep Facial Priors Into Landmarks for Privacy Preserving Multimodal Depression Recognition59 cited
  • 2024Two-Stage Temporal Modelling Framework for Video-Based Depression Recognition Using Graph Representation55 cited
  • 2024A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video53 cited
  • 2017ISLA: Temporal Segmentation and Labeling for Audio-Visual Emotion Recognition51 cited
  • 2019Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition45 cited
  • 2014Inferring Depression and Affect from Application Dependent Meta Knowledge44 cited

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