It is commonly assumed that a person's emotional state can be readily inferred from his or
her facial movements, typically called emotional expressions or facial expressions. This …

Automatic facial expression recognition is essential for many potential applications. Thus,
having a clear overview on existing datasets that have been investigated within the …

Automated affective computing in the wild setting is a challenging problem in computer
vision. Existing annotated databases of facial expressions in the wild are small and mostly …

Traditional techniques for emotion recognition have focused on the facial expression
analysis only, thus providing limited ability to encode context that comprehensively …

Current artificial intelligence systems for determining a person's emotions rely heavily on lip
and mouth movement and other facial features such as eyebrows, eyes, and the forehead …

We present an approach that combines automatic features learned by convolutional neural
networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) …

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 …

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 …

Most of the existing work on automatic facial expression analysis focuses on discrete
emotion recognition, or facial action unit detection. However, facial expressions do not …