Amongst these methods a very popular one is the constellation model which refers to those schemes which seek to detect a small number of features and their relative positions to then determine whether or not the object of interest is present.
These models build on the original idea of Fischler and Elschlager[1] of using the relative position of a few template matches and evolve in complexity in the work of Perona and others.
Early efforts, such as those by Yuille, Hallinan and Cohen[3] sought to detect facial features and fit deformable templates to them.
Yuille, Hallinan and Cohen's algorithm does have trouble finding the global minimum fit for a given model and so templates did occasionally become mismatched.
These three classes of algorithms naturally fall within the scope of template matching[7] Of the non-constellation perhaps the most successful is that of Leibe and Schiele.
The algorithm then takes a test image and runs an interest point locater (hopefully one of the scale invariant variety).