In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …

The convolutional neural network (CNN) has been widely used in image recognition field
due to its good performance. This paper proposes a facial expression recognition method …

Facial expression recognition has been an active area of research over the past few
decades, and it is still challenging due to the high intra-class variation. Traditional …

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 …

Facial expression recognition is a hot research topic and can be applied in many computer
vision fields, such as human–computer interaction, affective computing and so on. In this …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Past research on facial expressions have used relatively limited datasets, which makes it
unclear whether current methods can be employed in real world. In this paper, we present a …