  Palmprints are private and stable information for biometric recognition. In
the deep learning era, the development of palmprint recognition is limited by
the lack of sufficient training data. In this paper, by observing that palmar
creases are the key information to deep-learning-based palmprint recognition,
we propose to synthesize training data by manipulating palmar creases.
Concretely, we introduce an intuitive geometric model which represents palmar
creases with parameterized B\'ezier curves. By randomly sampling B\'ezier
parameters, we can synthesize massive training samples of diverse identities,
which enables us to pretrain large-scale palmprint recognition models.
Experimental results demonstrate that such synthetically pretrained models have
a very strong generalization ability: they can be efficiently transferred to
real datasets, leading to significant performance improvements on palmprint
recognition. For example, under the open-set protocol, our method improves the
strong ArcFace baseline by more than 10\% in terms of TAR@1e-6. And under the
closed-set protocol, our method reduces the equal error rate (EER) by an order
of magnitude.
