Reading discrete facial expressions
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Abstract What is it about a face that tells us whether a person is happy, sad, or angry? While most of us have not thought about these factors, the same cannot be said about the many researchers who have developed the field of emotion recognition in facial expressions and its three perspectives. The behavioral perspective traces the path of emotion recognition early in life and its controlling factors, including person-familiarity and parental abuse. The brain is at the center of the biological perspective, as fMRI and lesion studies have revealed specific neural structures used to discriminate emotions. Finally, the cognitive perspective debates the presence of situational and social influences, as well as whether certain parts of the face are more vital to recognizing emotions. This review will investigate recent studies that have provided major contributions to these three perspectives.
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Nonverbal Behavior Pioneer
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The movement of the shapes reflects how our emotions vary in strength and frequency in peoples lives. The States of an Emotion Each emotion names a number of related but different states. These states vary not only in their nature, but in their intensity. The Triggers of an Emotion Triggers automatically bring forth an emotion without consideration. The Actions of a State Each emotional state typically results in a number of actions.
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Silvan Tomkins deserves much of the credit for the renewed interest in facial expression which has developed in the last two decades. His affect theory emphasized the importance of the face, providing a new conceptual framework for considering expression and emotion. It was a framework for which emphasized the role of biology and which conceived of emotion in terms of eight discrete, quite different affects, rather than two or three affective dimensions.
Resources and Help Understanding Discrete Facial Expressions in Video Using an Emotion Avatar Image Abstract: Existing video-based facial expression recognition techniques analyze the geometry-based and appearance-based information in every frame as well as explore the temporal relation among frames. On the contrary, we present a new image-based representation and an associated reference image called the emotion avatar image EAI , and the avatar reference, respectively. This representation leverages the out-of-plane head rotation. It is not only robust to outliers but also provides a method to aggregate dynamic information from expressions with various lengths. The approach to facial expression analysis consists of the following steps: 1 face detection; 2 face registration of video frames with the avatar reference to form the EAI representation; 3 computation of features from EAIs using both local binary patterns and local phase quantization; and 4 the classification of the feature as one of the emotion type by using a linear support vector machine classifier.