In recent years, facial expression recognition (FER) has garnered significant attention within
the realm of computer vision research. This paper presents an innovative network called the …

Facial expression recognition (FER) in the wild is challenging due to the disturbing factors
including pose variation, occlusions, and illumination variation. The attention mechanism …

Facial expression recognition (FER) in-the-wild is challenging due to unconstraint settings
such as varying head poses, illumination, and occlusions. In addition, the performance of a …

In complex real-world situations, problems such as illumination changes, facial occlusion,
and variant poses make facial expression recognition (FER) a challenging task. To solve the …

Facial expression recognition (FER) becomes more challenging in the wild due to
unconstrained conditions, such as the different illumination, pose changes, and occlusion of …

One key challenge in facial expression recognition (FER) is the extraction of discriminative
features from critical facial regions. Because of their promising ability to learn discriminative …

Although great progress has been made in facial expression recognition, it still faces
challenges such as occlusion and pose changes in real-world scenario. To address this …

In facial expression recognition (FER), global and local features obtained from the same
face may have different recognition accuracy, indicating that they have different advantages …

One of the key challenge issues of deep-learning-based facial expression recognition (FER)
is learning effective and robust features from variant samples. In this paper, Region-based …

Facial expression recognition (FER) in the wild is an exceedingly challenging task in
computer vision due to subtle differences, poses, occlusions, label bias, and other …