An Ensemble of Face Recognition Algorithms for Unsupervised Expansion of Training Data

Facial recognition is a classical problem in computer vision. The accuracy of face recognition algorithms is crucial in practice, as systems are increasingly secured with biometric locks. However, the performance of these algorithms is heavily dependent upon the size of the training data. This paper proposes an unsupervised ensemble method for expanding the set of training faces when only a single labeled face per subject is known. We show that the ensemble's confidence measure is sufficient to expand the training set to the point where more sophisticated algorithms can take over in classification.