Facial expression recognition (FER) is a challenging task due to different expressions under
arbitrary poses. Most conventional approaches either perform face frontalization on a non …

Abstract Facial Expression Recognition (FER) has long been a challenging task in the field
of computer vision. In this paper, we present a novel model, named Deep Attentive Multi …

To avoid the complex process of explicit feature extraction in traditional facial expression
recognition, a face expression recognition method based on a convolutional neural network …

A key challenge of facial expression recognition (FER) is to develop effective
representations to balance the complex distribution of intra-and inter-class variations. The …

Facial expression recognition (FER) has long been a challenging task in computer vision. In
this paper, we propose a novel method, named deep comprehensive multipatches …

Facial Expression Recognition (FER), as the primary processing method for non-verbal
intentions, is an important and promising field of computer vision and artificial intelligence …

Deep neural network (DNN) has been used as a learning model for modeling the
hierarchical architecture of human brain. However, DNN suffers from problems of learning …

Driven by recent advances in human-centered computing, Facial Expression Recognition
(FER) has attracted significant attention in many applications. However, most conventional …

Automatic facial emotion recognition in real-world situations like partial occlusions, varying
head poses and illumination conditions are challenging to the machine learning community …

Pose-invariant facial expression recognition is quite challenging due to variations in facial
appearance and self-occlusion caused by head rotations. In this paper, we propose an …