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 …

Facial expression recognition (FER) in the wild received broad concerns in which occlusion
and pose variation are two key issues. This paper proposed a global multi-scale and local …

Recently, facial expression recognition (FER) in the wild has gained a lot of researchers'
attention because it is a valuable topic to enable the FER techniques to move from the …

Facial Expression Recognition (FER) is a classification task that points to face variants.
Hence, there are certain affinity features between facial expressions, receiving little attention …

Most research on facial expression recognition (FER) is conducted in highly controlled
environments, but its performance is often unacceptable when applied to real-world …

Recognizing the expressions of partially occluded faces is a challenging computer vision
problem. Previous expression recognition methods, either overlooked this issue or resolved …

Facial Expression Recognition (FER) plays a crucial role in the real-world applications.
However, large-scale FER datasets collected in the wild usually contain noises. More …

To achieve effective facial expression recognition (FER), it is of great importance to address
various disturbing factors, including pose, illumination, identity, and so on. However, a …

Facial expression recognition (FER) in the wild is challenging due to various unconstrained
conditions, ie, occlusions and head pose variations. Previous methods tend to improve the …

Human emotion is expressed, perceived and captured using a variety of dynamic data
modalities, such as speech (verbal), videos (facial expressions) and motion sensors (body …