Learning from a single labeled face and a stream of unlabeled data

Face recognition from a single image per person is a challenging problem because the training sample is extremely small. We study a variation of this problem. In our setting, only a single image of a single person is labeled, and all other people are unlabeled. This setting is very common in authentication on personal computers and mobile devices, and poses an additional challenge because it lacks negative examples. We formalize our problem as one-class classification, and propose and analyze an algorithm that learns a non-parametric model of the face from a single labeled image and a stream of unlabeled data. In many domains, for instance when a person interacts with a computer with a camera, unlabeled data are abundant and easy to utilize. We show how unlabeled data can help in learning better models and evaluate our method on 43 people. The people are identified 90% of the time at nearly zero false positives. This is 15% more often than by Fisherfaces at the same false positive rate. Finally, we conduct a comprehensive sensitivity analysis of our method and provide a guideline for setting its parameters.