  Palmprint recently shows great potential in recognition applications as it is
a privacy-friendly and stable biometric. However, the lack of large-scale
public palmprint datasets limits further research and development of palmprint
recognition. In this paper, we propose a novel realistic pseudo-palmprint
generation (RPG) model to synthesize palmprints with massive identities. We
first introduce a conditional modulation generator to improve the intra-class
diversity. Then an identity-aware loss is proposed to ensure identity
consistency against unpaired training. We further improve the B\'ezier palm
creases generation strategy to guarantee identity independence. Extensive
experimental results demonstrate that synthetic pretraining significantly
boosts the recognition model performance. For example, our model improves the
state-of-the-art B\'ezierPalm by more than $5\%$ and $14\%$ in terms of
TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. When accessing only
$10\%$ of the real training data, our method still outperforms ArcFace with
$100\%$ real training data, indicating that we are closer to real-data-free
palmprint recognition.
