TBM offers an interesting premise. Without intending to provide a comprehensive explanation of the TBM framework, a key point is that it introduces degrees of belief and transfer (giving rise to the name of the method: the transferable belief model), which allows the model to make the necessary assumptions required to perform adequate classification (of the expressions). Basically, this means it scores its assumptions, that is, the assumption that the expression is a happy expression is determined to have a n percentage chance of being correct (we'll see this in action later in this chapter when we review the results of our project).
Further (and I'm oversimplifying), TBM looks to use quantified beliefs to make its classification decisions. ...
