The emergence of artificial emotional intelligence technology is revolutionizing the fields of
computers and robotics, allowing for a new level of communication and understanding of …

Though deep neural networks (DNNs) have demonstrated excellent performance in
computer vision, they are susceptible and vulnerable to carefully crafted adversarial …

Abstract Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that
focuses on recognizing facial expressions in video format. Previous research has …

Driving is an integral part of daily life for millions of people worldwide, and it has a profound
impact on road safety and human health. The emotional state of the driver, including feelings …

Facial affective behavior analysis (FABA) is crucial for understanding human mental states
from images. However, traditional approaches primarily deploy models to discriminate …

Current works of facial expression learning in video consume significant computational
resources to learn spatial channel feature representations and temporal relationships. To …

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations,
eg, insufficient quantity and diversity of pose, occlusion and illumination, as well as the …

Dynamic facial expression recognition (DFER) is essential to the development of intelligent
and empathetic machines. Prior efforts in this field mainly fall into supervised learning …

Driving style detection is an essential real-world requirement in diverse contexts, such as
traffic safety, car insurance and fuel consumption optimization. However, the existing …

Compared with the image-based static facial expression recognition (SFER) task, the
dynamic facial expression recognition (DFER) task based on video sequences is closer to …