Unsupervised learning of facial expression components
Egede, Joy Onyekachukwu (2013) Unsupervised learning of facial expression components. [Dissertation (University of Nottingham only)]
The face is one of the most important means of non-verbal communication. A lot of information can be gotten about the emotional state of a person just by merely observing their facial expression. This is relatively easy in face to face communication but not so in human computer interaction. Supervised learning has been widely used by researchers to train machines to recognise facial expressions just like humans. However, supervised learning has significant limitations one of which is the fact that it makes use of 'labelled' facial images to train models to identify facial actions. It is very expensive and time consuming to label face images. It takes about an hour to label a 5min video. In addition, more than 7000 distinct facial expressions can be created from a combination of different facial muscle actions . The amount of labelled face images available for facial expression analysis is limited in supply and do not cover all the possible facial expressions. On the other hand, it is quite easy to collect or record a large amount of raw unlabelled data of people communicating without incurring so much cost.
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