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 emotion recognition (FER) is significant for human-computer interaction such as
clinical practice and behavioral description. Accurate and robust FER by computer models …

The primary neural networks' decision-making units are activation functions. Moreover, they
evaluate the output of networks neural node; thus, they are essential for the performance of …

In facial expression recognition (FER), the uncertainties introduced by inherent noises like
ambiguous facial expressions and inconsistent labels raise concerns about the credibility of …

Learning discriminative features for Facial Expression Recognition (FER) in the wild using
Convolutional Neural Networks (CNNs) is a non-trivial task due to the significant intra-class …

Human facial emotion recognition (FER) has attracted the attention of the research
community for its promising applications. Mapping different facial expressions to the …

Facial expression recognition in the wild is challenging due to various unconstrained
conditions. Although existing facial expression classifiers have been almost perfect on …

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 …

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 …

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 …