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ICP Experiments

By exploiting more information in this problem domain we can demonstrate various things:

• ICP based on advanced geometry and richer characteristics can yield better registration performance based on the resultant model built with it. By varying parameters in ICP graphs can be produced help select better value/s for particular data of greater extent. Shown in the graph we may choose to have a level of distribution - however we may choose to approximate it - assuming quite rightly that better registration will yield more concise descriptions (Occam's razor principle).
• A trickier thing to do, either for technical reasons or for purely computational limitations, is to use models as a similarity measure in an objective function for face analysis. This can be tested on coarser representations of faces, perhaps icon-sizes ones at a resolution far lower than the original.
• Expression recognition or expression-agnostic face recognition can be done using the above tools, which generally require further refinement. Data for this is already available. However, the exact method of choice for similarity must be strictly defined and tested systematically for compelling validation.

By making alterations and putting them back together into the code it was made possible to run several older variants of ICP algorithms, incorporating them into the pipeline of the program. Older implementations (even yours from 2008) can now be compared based on face data.

Their assessment is to be done with PCA that estimates complexity; the drawback of this approach, however, is that is becomes slow when the dataset is large. In the past, sets as small as 10 could be sufficient for an objective function in non-rigid registration. Figures and show the type of data we deal with.

Figure: ExamExample points cloud for ICP to register

Figure: On the left: two faces (with binary masks cropping them for rigid parts like nose and forehead) overlaid for ICP; on the right: same from another angle