Deep facial expression recognition: A survey
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 …
challenging in-the-wild conditions and the recent success of deep learning techniques in …
Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects
Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper
taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic …
taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic …
Deep learning for human affect recognition: Insights and new developments
Automatic human affect recognition is a key step towards more natural human-computer
interaction. Recent trends include recognition in the wild using a fusion of audiovisual and …
interaction. Recent trends include recognition in the wild using a fusion of audiovisual and …
Context-aware emotion recognition networks
Traditional techniques for emotion recognition have focused on the facial expression
analysis only, thus providing limited ability to encode context that comprehensively …
analysis only, thus providing limited ability to encode context that comprehensively …
Video-based emotion recognition using CNN-RNN and C3D hybrid networks
In this paper, we present a video-based emotion recognition system submitted to the EmotiW
2016 Challenge. The core module of this system is a hybrid network that combines recurrent …
2016 Challenge. The core module of this system is a hybrid network that combines recurrent …
Emotion recognition in speech using cross-modal transfer in the wild
Obtaining large, human labelled speech datasets to train models for emotion recognition is a
notoriously challenging task, hindered by annotation cost and label ambiguity. In this work …
notoriously challenging task, hindered by annotation cost and label ambiguity. In this work …
Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond
Automatic understanding of human affect using visual signals is of great importance in
everyday human–machine interactions. Appraising human emotional states, behaviors and …
everyday human–machine interactions. Appraising human emotional states, behaviors and …
Context based emotion recognition using emotic dataset
In our everyday lives and social interactions we often try to perceive the emotional states of
people. There has been a lot of research in providing machines with a similar capacity of …
people. There has been a lot of research in providing machines with a similar capacity of …
Mer 2023: Multi-label learning, modality robustness, and semi-supervised learning
The first Multimodal Emotion Recognition Challenge (MER 2023) 1 was successfully held at
ACM Multimedia. The challenge focuses on system robustness and consists of three distinct …
ACM Multimedia. The challenge focuses on system robustness and consists of three distinct …
Video-based emotion recognition in the wild using deep transfer learning and score fusion
Multimodal recognition of affective states is a difficult problem, unless the recording
conditions are carefully controlled. For recognition “in the wild”, large variances in face pose …
conditions are carefully controlled. For recognition “in the wild”, large variances in face pose …