Autonomous facial expression recognition using the facial action coding system

dc.contributor.advisorGhaziasgar, Mehrdad
dc.contributor.advisorConnan, James
dc.contributor.authorde la Cruz, Nathan
dc.date.accessioned2016-06-29T09:34:46Z
dc.date.accessioned2024-10-30T14:00:55Z
dc.date.available2016-06-29T09:34:46Z
dc.date.available2024-10-30T14:00:55Z
dc.date.issued2016
dc.description>Magister Scientiae - MScen_US
dc.description.abstractThe South African Sign Language research group at the University of the Western Cape is in the process of creating a fully-edged machine translation system to automatically translate between South African Sign Language and English. A major component of the system is the ability to accurately recognise facial expressions, which are used to convey emphasis, tone and mood within South African Sign Language sentences. Traditionally, facial expression recognition research has taken one of two paths: either recognising whole facial expressions of which there are six i.e. anger, disgust, fear, happiness, sadness, surprise, as well as the neutral expression; or recognising the fundamental components of facial expressions as defined by the Facial Action Coding System in the form of Action Units. Action Units are directly related to the motion of specific muscles in the face, combinations of which are used to form any facial expression. This research investigates enhanced recognition of whole facial expressions by means of a hybrid approach that combines traditional whole facial expression recognition with Action Unit recognition to achieve an enhanced classification approach.en_US
dc.identifier.urihttps://hdl.handle.net/10566/16980
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.rights.holderUniversity of the Western Capeen_US
dc.subjectFace detectionen_US
dc.subjectHaar featuresen_US
dc.subjectSupport vector machineen_US
dc.subjectSouth African sign languageen_US
dc.subjectFacial expression recognitionen_US
dc.titleAutonomous facial expression recognition using the facial action coding systemen_US

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