Article

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>

2017-07-10

Abstract excerpt

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence an...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b667de3c-46d5-537c-8872-6190072736be
DOI: 10.20944/preprints201706.0003.v2

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>DOI 10.20944/preprints201706.0003.v2Read

Modelling Human-Like Emotion Perception Using Multimodal Deep LearningSemantic proximity Read

Development of an Interactive Digital Human with Context-Sensitive Facial ExpressionsSemantic proximity Read

Facial Expressions as a Nexus for Health AssessmentSemantic proximity Read

ATHOS: a dATabase of 48 3D Human virtual characters with non-emOtional facial expreSsion for virtual realitySemantic proximity Read

Machine and human emotion classification diverge for naturalistic imagesSemantic proximity Read

Emotion recognition from posed and spontaneous dynamic expressions: Human observers vs. machine analysisSemantic proximity Read

Using Computer-vision and Machine Learning to Automate Facial Coding of Positive and Negative Affect IntensitySemantic proximity Read

Efficient Unsupervised Clustering of Facial Geometry and Head Orientation Using 2D LandmarksSemantic proximity Read

A Hybrid Deep Learning Framework for efficient emotion detection on the facial image datasetSemantic proximity Read

Observer-Generated Maps of Diagnostic Facial Features Enable Categorization and Prediction of Emotion ExpressionsSemantic proximity Read

Facial Expressions of Emotion Categories are Embedded within a Dimensional Space of Valence-arousalSemantic proximity Read

Are people happy when they smile? Affective assessments based on automatic smile genuineness identificationSemantic proximity Read