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 an emerging and multifaceted research topic.
Applications of FER in healthcare, security, safe driving, and so forth have contributed to the …

Occlusion and pose variations, which can change facial appearance significantly, are two
major obstacles for automatic Facial Expression Recognition (FER). Though automatic FER …

Analyzing human multimodal language is an emerging area of research in NLP. Intrinsically
this language is multimodal (heterogeneous), sequential and asynchronous; it consists of …

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 …

Past research on facial expressions have used relatively limited datasets, which makes it
unclear whether current methods can be employed in real world. In this paper, we present a …

Research in face perception and emotion theory requires very large annotated databases of
images of facial expressions of emotion. Annotations should include Action Units (AUs) and …

This paper presents the techniques employed in our team's submissions to the 2015
Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression …

Automatic understanding of human affect using visual signals is of great importance in
everyday human–machine interactions. Appraising human emotional states, behaviors and …

Relatively small data sets available for expression recognition research make the training of
deep networks very challenging. Although fine-tuning can partially alleviate the issue, the …