用边缘检测生成逼真掌纹，支持大规模可控身份合成。
Canny2Palm: Realistic and Controllable Palmprint Generation for Large-scale Pre-training
- 用Canny边缘提取纹理，结合Pix2Pix生成逼真掌纹。
- 生成1万种新身份后性能仍提升，超越现有方法。
- 适合需要大量可控生物特征数据的研究者。
掌纹识别是一种安全且保护隐私的生物识别方式。提高掌纹识别准确率的主要挑战在于真实掌纹数据稀缺。近年来，利用虚拟掌纹进行大规模预训练成为研究热点。本文提出一种名为Canny2Palm的新合成方法：通过Canny边缘检测器提取掌纹纹理，并将其作为条件输入Pix2Pix网络，生成逼真掌纹图像。通过重新组合不同身份的纹理，可生成新的身份，只需在生成器中引入新组合即可。Canny2Palm不仅能生成符合真实掌纹分布的数据，还能实现可控多样性，支持大规模新身份生成。在开放集掌纹识别基准测试中，使用该方法生成的合成数据预训练模型，识别准确率最高提升7.2%。此外，当合成身份达到10,000个时，模型性能仍持续上升，而现有方法已趋于饱和，表明该方法在大规模预训练中具有巨大潜力。
原文摘要 · Abstract (English)
Palmprint recognition is a secure and privacy-friendly method of biometric identification. One of the major challenges to improve palmprint recognition accuracy is the scarcity of palmprint data. Recently, a popular line of research revolves around the synthesis of virtual palmprints for large-scale pre-training purposes. In this paper, we propose a novel synthesis method named Canny2Palm that extracts palm textures with Canny edge detector and uses them to condition a Pix2Pix network for realistic palmprint generation. By re-assembling palmprint textures from different identities, we are able to create new identities by seeding the generator with new assemblies. Canny2Palm not only synthesizes realistic data following the distribution of real palmprints but also enables controllable diversity to generate large-scale new identities. On open-set palmprint recognition benchmarks, models pre-trained with Canny2Palm synthetic data outperform the state-of-the-art with up to 7.2% higher identification accuracy. Moreover, the performance of models pre-trained with Canny2Palm continues to improve given 10,000 synthetic IDs while those with existing methods already saturate, demonstrating the potential of our method for large-scale pre-training.
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