Encouraging results come about with the changes to the ICP routines, adding to what previously was tested at another level, namely model and residual. With improved smoothing and with GIP's v1 of ICP (older) the results are improved significantly. Visual examples with and without translation (but with GIP's v2 of ICP) are shown...
Improving this further with rotation and a model that annuls translation and rotation (currently it does not) should be trivial, with additional refinements incurred by use of larger models and simpler sets (the ones currently used are full of different expressions).
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ICP experiments were run until papers were explored again. One experiment in the pipeline strives to emulate ICP as described in Mian's earlier papers, on which he based his Ph.D. (he did it at the same time as myself).
Upon completion of a larger experiment it turned out that it was designed incorrectly because ICP - with Mian-style translation18 - did not work correctly and therefore the model built was improper. It did occur, however, that despite this fluke there is decent capability within the new model to detect pairs (maybe an accidental discovery worth exploring in the future). The results are shown in Figure .
Our newly-combined set of Spring and Fall Semester builds a model with translation, where the ICP algorithm is as the same as UWA's. The test sets are also of Spring and Fall Semester, but they have no overlap with respect to the training set and they still have many expressions in them. To distinguish between expression differences (intra-personal) and inter-personal differences we use a coarse model which is fast to build and match to.
Unintended arrival at experiments where a model-based approach by far outperforms the median-based are probably not of high priority at this stage.
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To begin the exploration of robust PCA as proposed by Yi Ma (of MSR- China, a copy of his book (350+ pages) was obtained, hopefully with concise summaries too.
Roy Schestowitz 2012-01-08