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 …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Facial expression recognition (FER) is a challenging task due to different expressions under
arbitrary poses. Most conventional approaches either perform face frontalization on a non …

A training process for facial expression recognition is usually performed sequentially in three
individual stages: feature learning, feature selection, and classifier construction. Extensive …

In this paper, a novel deep neural network (DNN)-driven feature learning method is
proposed and applied to multi-view facial expression recognition (FER). In this method …

Despite being the appearance-based classifier of choice in recent years, relatively few
works have examined how much convolutional neural networks (CNNs) can improve …

Bayesian explanations have swept through cognitive science over the past two decades,
from intuitive physics and causal learning, to perception, motor control and language. Yet …

Much recent research has been devoted to learning algorithms for deep architectures such
as Deep Belief Networks and stacks of auto-encoder variants with impressive results being …

Facial expression editing is a challenging task as it needs a high-level semantic
understanding of the input face image. In conventional methods, either paired training data …

Driven by recent advances in human-centered computing, Facial Expression Recognition
(FER) has attracted significant attention in many applications. However, most conventional …