Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data

This paper presents probabilistic modeling methods to solve the problem of dis(cid:173) criminating between five facial orientations with very little labeled data. 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 unla(cid:173) beled images are readily available. Through a large set of empirical tests, we examine the benefits of unlabeled data for each of the models.