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.

翻译：掌纹作为一种隐私友好且稳定的生物特征，近年来在识别应用中展现出巨大潜力。然而，大规模公共掌纹数据集的缺乏限制了掌纹识别的进一步研究与发展。本文提出一种新颖的逼真伪掌纹生成（RPG）模型，可合成包含海量身份的掌纹图像。我们首先引入条件调制生成器以提升类内多样性，随后提出身份感知损失函数以确保非配对训练下的身份一致性，并进一步改进贝塞尔掌纹折痕生成策略以保障身份独立性。大量实验结果表明，合成预训练显著提升了识别模型性能。例如，在1:1和1:3开放集协议下，本模型在TAR@FAR=1e-6指标上较当前最优模型BézierPalm分别提升超过5%和14%。当仅使用10%真实训练数据时，本方法仍优于使用100%真实训练数据的ArcFace方法，这标志着我们正逐步接近无需真实数据的掌纹识别。