Face recognition algorithms based on linear discriminant analysis (LDA) generally give satisfactory performance but tend to require a relatively high number of samples in order
to learn reliable projections. In many practical applications of face recognition there is only a small number of labelled face images and in this case LDA-based algorithms generally lead to poor performance. The contributions in this paper relate to a new semi-supervised, self-training LDA-based algorithm which is used to augment a manually labelled training set with new data from an unlabelled, auxiliary set and hence to improve recognition performance.
Without the cost of manual labelling such auxiliary data is often easily acquired but is not normally useful for learning. We report face recognition experiments on 3 independent
databases which demonstrate a constant improvement of our baseline, supervised LDA system. The performance of our algorithm is also shown to significantly outperform other
semi-supervised learning algorithms.
