Semi-Supervised Face Detection

Abstract

This paper presents a discussion on semi-supervised learning of probabilistic mixture model classifiers for face detection. We present a theoretical analysis of semi-supervised learning and show that there is an overlooked fundamental difference between the purely supervised and the semi-supervised learning paradigms. While in the supervised case, increasing the amount of labeled training data is always seen as a way to improve the classifier's performance, the converse might also be true as the number of unlabeled data is increased in the semi-supervised case. We also study the impact of this theoretical finding on Bayesian network classifiers, with the goal of avoiding the performance degradation with unlabeled data. We apply the semi-supervised approach to face detection and we show that learning the structure of Bayesian network classifiers enables learning good classifiers for face detection with a small labeled set and a large unlabeled set.