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 …

With the recent renaissance of deep convolutional neural networks (CNNs), encouraging
breakthroughs have been achieved on the supervised recognition tasks, where each class …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

Automatic human affect recognition is a key step towards more natural human-computer
interaction. Recent trends include recognition in the wild using a fusion of audiovisual and …

Human action recognition targets recognising different actions from a sequence of
observations and different environmental conditions. A wide different applications is …

Automatic emotion recognition is the process of identifying human emotion from signals
such as facial expression, speech, and text. Collecting and labeling such signals is often …

While machine learning approaches to visual emotion recognition offer great promise,
current methods consider training and testing models on small scale datasets covering …

Video is an important medium in communication and entertainment, and thus, an intelligent
understanding of videos has attracted widespread interest in academic community. Video …

Multimodal emotion understanding enables AI systems to interpret human emotions. With
accelerated video surge, emotion understanding remains challenging due to inherent data …

Automated deception detection (ADD) from real-life videos is a challenging task. It
specifically needs to address two problems:(1) Both face and body contain useful cues …