Extracting and understanding of emotion is of high importance for the interaction among human and machine communication systems. The most expressive way to display the human’s emotion is through facial expression analysis. This paper presents and implements an automatic extraction and recognition method of facial expression and emotion from still image. There are two steps to recognize the faci...

The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint The process of rrecognizing facial expression is a hard task due to the several circumstances such as facial ...

To investigate the impact of spatial frequency on emotional facial expression analysis, ERPs were recorded in response to low spatial frequency (LSF), high spatial frequency (HSF), and unfiltered broad spatial frequency (BSF) faces with fearful or neutral expressions, houses, and chairs. In line with previous findings, BSF fearful facial expressions elicited a greater frontal positivity than BS...

When people see a facial expression displayed by another individual, they experience changes in multiple components of emotion, such as appraisals, action tendencies, physiological and motor responses, subjective feelings. Facial expressions can thus be regarded emotion-eliciting stimuli. It has often been assumed that the kind emotion evoked certain is same one conveyed expression: e.g., happi...

This paper investigates the appearance manifold of facial expression: embedding image sequences of facial expression from the high dimensional appearance feature space to a low dimensional manifold. We explore Locality Preserving Projections (LPP) to learn expression manifolds from two kinds of feature space: raw image data and Local Binary Patterns (LBP). For manifolds of different subjects, w...

Facial expressions are a kind of nonverbal communication. They carry the state of emotion of a person. Facial expression plays an important role in face-to face human-computer communication. Automatic facial expression synthesis became popular research area nowadays. It can be used in many areas such that physiology, education, murder squad, analysis of tendency to crime to get a clue about men...

Human face analysis is an important task in computer vision. According to cognitive-psychological studies, facial dynamics could provide crucial cues for face analysis. In particular, the motion of facial local regions in facial expression is related to the motion of other facial regions. In this paper, a novel deep learning approach which exploits the relations of facial local dynamics has bee...

Automatic facial expression recognition has gained much attention during the last decade because of its potential application in areas such as more engaging human-computer interfaces. Automatic facial expression recognition is a sub-area of face analysis research that is based heavily on methods of computer vision, machine learning, and image processing. Many efforts either to create a novel or...

In this paper, we consider facial expression recognition using an unsupervised learning framework. Specifically, given a data set composed of a number of facial images of the same subject with different facial expressions, the algorithm segments the data set into groups corresponding to different facial expressions. Each facial image can be regarded as a point in a high-dimensional space, and t...