This paper presents interpolation using two-stage PCA for hand shape recognition. In the first stage PCA is performed on the entire training dataset of real human hand images. In the second stage, on separate sub-sets of the projected points in the first-stage eigenspace. The training set contains only a few pose angles. The output is a set of new interpolated manifolds, representing the missing data. The goal of this approach is to create a more robust dataset, able to recognise a hand image from an unknown rotation. We show some accuracy values in recognising unknown hand shapes.
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