用光流模拟真实手掌变形，生成更逼真的合成掌纹
FlowPalm: Optical Flow Driven Non-Rigid Deformation for Geometrically Diverse Palmprint Generation
- 基于真实掌纹对的光流估计，学习复杂非刚性形变规律
- 渐进式采样在扩散过程中引入形变，保持身份一致性
- 在6个数据集上显著提升识别性能，适合掌纹识别研究
近年来，合成掌纹被广泛用于替代真实数据训练识别模型。为有效提升性能，合成数据需反映真实掌纹的多样性，包括风格差异与几何差异。然而，现有方法多聚焦于风格迁移，几何变化或被忽略，或仅通过简单手工增强近似。本文提出FlowPalm，一种基于光流驱动的掌纹生成框架，可模拟真实手掌中的复杂非刚性形变。具体地，该方法通过估计真实掌纹对之间的光流，捕捉几何形变的统计模式；在此先验基础上，设计渐进采样过程，在扩散生成中逐步引入形变并保持身份一致。在六个基准数据集上的大量实验表明，FlowPalm在下游识别任务中显著优于当前最优的掌纹生成方法。
原文摘要 · Abstract (English)
Recently, synthetic palmprints have been increasingly used as substitutes for real data to train recognition models. To be effective, such synthetic data must reflect the diversity of real palmprints, including both style variation and geometric variation. However, existing palmprint generation methods mainly focus on style translation, while geometric variation is either ignored or approximated by simple handcrafted augmentations. In this work, we propose FlowPalm, an optical-flow-driven palmprint generation framework capable of simulating the complex non-rigid deformations observed in real palms. Specifically, FlowPalm estimates optical flows between real palmprint pairs to capture the statistical patterns of geometric deformations. Building on these priors, we design a progressive sampling process that gradually introduces the geometric deformations during diffusion while maintaining identity consistency. Extensive experiments on six benchmark datasets demonstrate that FlowPalm significantly outperforms state-of-the-art palmprint generation approaches in downstream recognition tasks. Project page: https://yuchenzou.github.io/FlowPalm/
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