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Facial coding is the process of measuring human emotions through facial expressions. Emotions can be detected by computer algorithms for automatic emotion recognition that record facial expressions via webcam. This can be applied to better understanding of people’s reactions to visual stimuli.
Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context.
The emotion annotation can be done in discrete emotion labels or on a continuous scale. Most of the databases are usually based on the basic emotions theory (by Paul Ekman) which assumes the existence of six discrete basic emotions (anger, fear, disgust, surprise, joy, sadness). However, some databases include the emotion tagging in continuous ...
The face expresses a great deal of emotion, however, there are two main facial muscle groups that are usually studied to detect emotion: The corrugator supercilii muscle, also known as the 'frowning' muscle, draws the brow down into a frown, and therefore is the best test for negative, unpleasant emotional response.↵The zygomaticus major ...
As AUs are independent of any interpretation, they can be used for any higher order decision making process including recognition of basic emotions, or pre-programmed commands for an ambient intelligent environment. The FACS manual is over 500 pages in length and provides the AUs, as well as Ekman's interpretation of their meanings.
The response format that is most commonly used in emotion recognition studies is forced choice. In forced choice, for each facial expression, participants are asked to select their response from a short list of emotion labels. The forced choice method determines the emotion attributed to the facial expressions via the labels that are presented ...
Real-time face detection in video footage became possible in 2001 with the Viola–Jones object detection framework for faces. [28] Paul Viola and Michael Jones combined their face detection method with the Haar-like feature approach to object recognition in digital images to launch AdaBoost, the first real-time frontal-view face detector. [29]
Microexpressions can be difficult to recognize, but still images and video can make them easier to perceive. In order to learn how to recognize the way that various emotions register across parts of the face, Ekman and Friesen recommend the study of what they call "facial blueprint photographs", photographic studies of "the same person showing all the emotions" under consistent photographic ...