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Abstract:
This paper presents probabilistic modeling methods to solve
the problem of discriminating between five facial orientations with
very little labeled data. Three models are explored. The first
model maintains no inter-pixel dependencies, the second model is
capable of modeling a set of arbitrary pair-wise dependencies, and
the last model allows dependencies only between neighboring pixels.
We show that for all three of these models, the accuracy of the
learned models can be greatly improved by augmenting a small number
of labeled training images with a large set of unlabeled images
using Expectation-Maximization. This is important because it is
often difficult to obtain image labels, while many unlabeled images
are readily available. Through a large set of empirical tests, we
examine the benefits of unlabeled data for each of the models. By
using only two randomly selected labeled examples per class, we can
discriminate between the five facial orientations with an accuracy
of 94; with six examples, we achieve an accuracy of 98.
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