Facial expression recognition (FER) is increasingly gaining importance in various emerging
affective computing applications. In practice, achieving accurate FER is challenging due to …

Facial expression is temporally dynamic event which can be decomposed into a set of
muscle motions occurring in different facial regions over various time intervals. For dynamic …

Deep learning algorithms have shown significant performance improvements for facial
expression recognition (FER). Most deep learning-based methods, however, focus more …

Capturing the dynamics of facial expression progression in video is an essential and
challenging task for facial expression recognition (FER). In this article, we propose an …

Emotion recognition is indispensable in human-machine interaction systems. It comprises
locating facial regions of interest in images and classifying them into one of seven classes …

One key challenging issues of facial expression recognition (FER) in video sequences is to
extract discriminative spatiotemporal video features from facial expression images in video …

Learning discriminative features is of vital importance for automatic facial expression
recognition (FER) in the wild. In this article, we propose a novel Slide-Patch and Whole-Face …

Previous methods for dynamic facial expression in the wild are mainly based on
Convolutional Neural Networks (CNNs), whose local operations ignore the long-range …

Video-based facial expression recognition is a long-standing problem owing to a gap
between visual features and emotions, difficulties in tracking the subtle movement of …

One key challenging issue of facial expression recognition is to capture the dynamic
variation of facial physical structure from videos. In this paper, we propose a part-based …