Palmprint recognition is widely used in biometric systems, yet real-world performance often degrades due to feature distribution shifts caused by heterogeneous deployment conditions. Most deep palmprint models assume a closed and stationary distribution, leading to overfitting to dataset-specific textures rather than learning domain-invariant representations. Although data augmentation is commonly used to mitigate this issue, it assumes augmented samples can approximate the target deployment distribution, an assumption that often fails under significant domain mismatch. To address this limitation, we propose PalmBridge, a plug-and-play feature-space alignment framework for open-set palmprint verification based on vector quantization. Rather than relying solely on data-level augmentation, PalmBridge learns a compact set of representative vectors directly from training features. During enrollment and verification, each feature vector is mapped to its nearest representative vector under a minimum-distance criterion, and the mapped vector is then blended with the original vector. This design suppresses nuisance variation induced by domain shifts while retaining discriminative identity cues. The representative vectors are jointly optimized with the backbone network using task supervision, a feature-consistency objective, and an orthogonality regularization term to form a stable and well-structured shared embedding space. Furthermore, we analyze feature-to-representative mappings via assignment consistency and collision rate to assess model's sensitivity to blending weights. Experiments on multiple palmprint datasets and backbone architectures show that PalmBridge consistently reduces EER in intra-dataset open-set evaluation and improves cross-dataset generalization with negligible to modest runtime overhead.

翻译：掌纹识别在生物识别系统中被广泛应用，然而在实际应用中，由于异构部署条件导致特征分布偏移，其性能往往下降。大多数深度掌纹模型假设数据分布是封闭且平稳的，导致模型过度拟合数据集特定的纹理特征，而非学习域不变的表征。尽管数据增强常被用于缓解此问题，但其假设增强样本能够近似目标部署分布，这一假设在显著的域不匹配情况下往往失效。为克服这一局限，本文提出PalmBridge，一种基于向量量化的、用于开放集掌纹验证的即插即用特征空间对齐框架。PalmBridge不依赖于单纯的数据级增强，而是直接从训练特征中学习一组紧凑的代表性向量。在注册和验证阶段，每个特征向量根据最小距离准则被映射到其最近邻的代表性向量，随后将映射后的向量与原始向量进行融合。该设计能够抑制由域偏移引起的干扰性变异，同时保留具有判别性的身份信息。代表性向量与骨干网络通过任务监督、特征一致性目标以及正交正则化项进行联合优化，以形成一个稳定且结构良好的共享嵌入空间。此外，我们通过分配一致性和碰撞率分析了特征到代表性向量的映射关系，以评估模型对融合权重的敏感性。在多个掌纹数据集和骨干架构上的实验表明，PalmBridge在数据集内开放集评估中持续降低等错误率（EER），并提升了跨数据集的泛化能力，同时仅带来可忽略至适度的运行时开销。