With the transition of facial expression recognition (FER) from laboratory-controlled to
challenging in-the-wild conditions and the recent success of deep learning techniques in …

Facial Expression Recognition (FER) is presently the aspect of cognitive and affective
computing with the most attention and popularity, aided by its vast application areas. Several …

Abstract Deep Neural Networks (DNNs) outperform traditional models in numerous optical
recognition missions containing Facial Expression Recognition (FER) which is an imperative …

Abstract Deep Neural Networks (DNNs) have shown to outperform traditional methods in
various visual recognition tasks including Facial Expression Recognition (FER). In spite of …

The three-dimensional convolutional neural network (3D-CNN) and long short-term memory
(LSTM) have consistently outperformed many approaches in video-based facial expression …

Facial expression recognition (FER) has received significant attention in the past decade
with witnessed progress, but data inconsistencies among different FER datasets greatly …

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 …

Recognition of facial expressions across various actors, contexts, and recording conditions
in real-world videos involves identifying local facial movements. Hence, it is important to …

Understanding a person's feelings is a very important process for the affective computing.
People express their emotions in various ways. Among them, facial expression is the most …

Datasets play an important role in the progress of facial expression recognition algorithms,
but they may suffer from obvious biases caused by different cultures and collection …