Results 11 to 20 of about 5,527,068 (374)

Facial expression and emotion.

open access: yesLaryngo- rhino- otologie, 2023
Human facial expressions are unique in their ability to express our emotions and communicate them to others. The mimic expression of basic emotions is very similar across different cultures and has also many features in common with other mammals. This suggests a common genetic origin of the association between facial expressions and emotion.
Klingner, Carsten M.   +1 more
openaire   +3 more sources

Facial Motion Prior Networks for Facial Expression Recognition [PDF]

open access: yesVisual Communications and Image Processing, 2019
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative ...
Cai, Jianfei   +4 more
core   +2 more sources

Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition [PDF]

open access: yesComputer Vision and Pattern Recognition, 2021
Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity.
Jiahui She   +5 more
semanticscholar   +1 more source

Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition [PDF]

open access: yesBiomimetics, 2021
This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception.
Zhengyao Wen   +3 more
semanticscholar   +1 more source

Facial Expression Recognition With Visual Transformers and Attentional Selective Fusion [PDF]

open access: yesIEEE Transactions on Affective Computing, 2021
Facial Expression Recognition (FER) in the wild is extremely challenging due to occlusions, variant head poses, face deformation and motion blur under unconstrained conditions.
Fuyan Ma, Bin Sun, Shutao Li
semanticscholar   +1 more source

Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition [PDF]

open access: yesComputer Vision and Pattern Recognition, 2021
In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities ...
Delian Ruan   +5 more
semanticscholar   +1 more source

Suppressing Uncertainties for Large-Scale Facial Expression Recognition [PDF]

open access: yesComputer Vision and Pattern Recognition, 2020
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators.
Kai Wang   +4 more
semanticscholar   +1 more source

Py-Feat: Python Facial Expression Analysis Toolbox [PDF]

open access: yesAffective Science, 2021
Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos.
J. H. Cheong   +3 more
semanticscholar   +1 more source

AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild [PDF]

open access: yesIEEE Transactions on Affective Computing, 2017
Automated affective computing in the wild setting is a challenging problem in computer vision. Existing annotated databases of facial expressions in the wild are small and mostly cover discrete emotions (aka the categorical model). There are very limited
A. Mollahosseini   +2 more
semanticscholar   +1 more source

Deep Facial Expression Recognition: A Survey [PDF]

open access: yesIEEE Transactions on Affective Computing, 2018
With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to ...
Shan Li, Weihong Deng
semanticscholar   +1 more source

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