Self-supervised vision transformer-based few-shot learning for facial expression recognition
X Chen, X Zheng, K Sun, W Liu, Y Zhang - Information Sciences, 2023 - Elsevier
Facial expression recognition (FER) is embedded in many real-world human-computer
interaction tasks, such as online learning, depression recognition and remote diagnosis …
interaction tasks, such as online learning, depression recognition and remote diagnosis …
Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images
Chest X-ray images are used in deep convolutional neural networks for the detection of
COVID-19, the greatest human challenge of the 21st century. Robustness to noise and …
COVID-19, the greatest human challenge of the 21st century. Robustness to noise and …
Generative adversarial networks in human emotion synthesis: A review
N Hajarolasvadi, MA Ramirez, W Beccaro… - IEEE …, 2020 - ieeexplore.ieee.org
Deep generative models have become an emerging topic in various research areas like
computer vision and signal processing. These models allow synthesizing realistic data …
computer vision and signal processing. These models allow synthesizing realistic data …
Adaptive deep disturbance-disentangled learning for facial expression recognition
In this paper, we propose a novel adaptive deep disturbance-disentangled learning (ADDL)
method for effective facial expression recognition (FER). ADDL involves a two-stage …
method for effective facial expression recognition (FER). ADDL involves a two-stage …
Semantic-rich facial emotional expression recognition
The ability to perceive human facial emotions is an essential feature of various multi-modal
applications, especially in the intelligent human-computer interaction (HCI) area. In recent …
applications, especially in the intelligent human-computer interaction (HCI) area. In recent …
Multi-facial patches aggregation network for facial expression recognition and facial regions contributions to emotion display
In this paper, an approach for Facial Expressions Recognition (FER) based on a multi-facial
patches (MFP) aggregation network is proposed. Deep features are learned from facial …
patches (MFP) aggregation network is proposed. Deep features are learned from facial …
Few-shot learning in emotion recognition of spontaneous speech using a siamese neural network with adaptive sample pair formation
K Feng, T Chaspari - IEEE Transactions on Affective Computing, 2021 - ieeexplore.ieee.org
Speech-based machine learning (ML) has been heralded as a promising solution for
tracking prosodic and spectrotemporal patterns in real-life that are indicative of emotional …
tracking prosodic and spectrotemporal patterns in real-life that are indicative of emotional …
Multi-modal residual perceptron network for audio–video emotion recognition
X Chang, W Skarbek - sensors, 2021 - mdpi.com
Emotion recognition is an important research field for human–computer interaction. Audio–
video emotion recognition is now attacked with deep neural network modeling tools. In …
video emotion recognition is now attacked with deep neural network modeling tools. In …
Emotion recognition from facial expressions using images with arbitrary poses using siamese network
S Ramakrishnan, N Upadhyay, P Das… - … on Smart Electronics …, 2021 - ieeexplore.ieee.org
Emotion Recognition through facial expressions has become an active area of research.
Challenges like pose and illumination variations in images increases the scope of research …
Challenges like pose and illumination variations in images increases the scope of research …
Deep multi-facial patches aggregation network for facial expression recognition
In this paper, we propose an approach for Facial Expressions Recognition (FER) based on a
deep multi-facial patches aggregation network. Deep features are learned from facial …
deep multi-facial patches aggregation network. Deep features are learned from facial …