AIMC Topic: Facial Expression

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VT-3DCapsNet: Visual tempos 3D-Capsule network for video-based facial expression recognition.

PloS one
Facial expression recognition(FER) is a hot topic in computer vision, especially as deep learning based methods are gaining traction in this field. However, traditional convolutional neural networks (CNN) ignore the relative position relationship of ...

TriCAFFNet: A Tri-Cross-Attention Transformer with a Multi-Feature Fusion Network for Facial Expression Recognition.

Sensors (Basel, Switzerland)
In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention trans...

Optimized efficient attention-based network for facial expressions analysis in neurological health care.

Computers in biology and medicine
Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods co...

Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human-Robot Interaction.

IEEE transactions on neural networks and learning systems
A coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracte...

Enhanced Hybrid Vision Transformer with Multi-Scale Feature Integration and Patch Dropping for Facial Expression Recognition.

Sensors (Basel, Switzerland)
Convolutional neural networks (CNNs) have made significant progress in the field of facial expression recognition (FER). However, due to challenges such as occlusion, lighting variations, and changes in head pose, facial expression recognition in rea...

Image-based facial emotion recognition using convolutional neural network on emognition dataset.

Scientific reports
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limi...

Facial micro-expression recognition using stochastic graph convolutional network and dual transferred learning.

Neural networks : the official journal of the International Neural Network Society
Micro-expression recognition (MER) has drawn increasing attention due to its wide application in lie detection, criminal detection and psychological consultation. However, the best recognition accuracy on recent public dataset is still low compared t...

Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions.

Orthodontics & craniofacial research
OBJECTIVE: In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions aff...

Systematic Review of Emotion Detection with Computer Vision and Deep Learning.

Sensors (Basel, Switzerland)
Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human-computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this ...

Portable Facial Expression System Based on EMG Sensors and Machine Learning Models.

Sensors (Basel, Switzerland)
One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy ...