System for training of recognition system using ad hoc training data

Abstract

A machine learning system to determine an identity of a user is trained using triplets of ad hoc synthetic data and actual data. The data may comprise multimodal images of a hand. Each triplet comprises an anchor, a positive, and a negative image. Synthetic triplets for different synthesized identities are generated on an ad hoc basis and provided as input during training of the machine learning system. The machine learning system uses a pairwise label-based loss function, such as a triplet loss function during training. Synthetic triplets may be generated to provide more challenging training data, to provide training data for categories that are underrepresented in the actual data, and so forth. The system uses substantially less memory during training, and the synthetic triplets need not be retained further reducing memory use. Ongoing training is supported as new actual triplets become available, and may be supplemented by additional synthetic triplets.