Doppelganger Mining for Face Representation Learning

Abstract

In this paper we present Doppelganger mining - a method to learn better face representations. The main idea of this method is to maintain a list with the most similar identities for each identity in the training set. This list is used to generate better mini-batches by sampling pairs of similar-looking identities ("doppelgangers") together. It is especially useful for methods, based on exemplar-based supervision. Usually hard example mining comes with a price of necessity to use large mini-batches or substantial extra computation and memory cost, particularly for datasets with large numbers of identities. Our method needs only a negligible extra computation and memory. In our experiments on a benchmark dataset with 21,000 persons we show that Doppelganger mining, being inserted in the face representation learning process with joint prototype-based and exemplar-based supervision, significantly improves the discriminative power of learned face representations.