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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.
A facial expression database is a collection of images or video clips with facial expressions of a range of emotions. Well-annotated ( emotion -tagged) media content of facial behavior is essential for training, testing, and validation of algorithms for the development of expression recognition systems .
Facial expressions are tracked in real time using key points on the viewer’s face to recognize a rich array of both emotional and cognitive states such as enjoyment, attention and confusion. Many of the users’ responses are so quick and fleeting that viewers may not even remember them, let alone be able to objectively report about them.
Face recognition, classification 2011 [111] Zhao, G. et al. BU-3DFE neutral face, and 6 expressions: anger, happiness, sadness, surprise, disgust, fear (4 levels). 3D images extracted. None. 2500 Images, text Facial expression recognition, classification 2006 [112] Binghamton University: Face Recognition Grand Challenge Dataset
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 ...
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]
Affective computing can be used to measure and to recognize emotional information in systems and devises employing affective haptics. Emotional information is extracted by using such techniques as speech recognition, natural language processing, facial expression detection, and measurement of physiological data.
Face detection can be used as part of a software implementation of emotional inference. Emotional inference can be used to help people with autism understand the feelings of people around them. [8] AI-assisted emotion detection in faces has gained significant traction in recent years, employing various models to interpret human emotional states.