PalmBridge: A Plug-and-Play Feature Alignment Framework for Open-Set Palmprint Verification

2026-01-28

AI Summary

This work addresses the performance degradation of palmprint recognition in cross-domain deployment caused by feature distribution shifts, a challenge inadequately mitigated by existing data augmentation techniques. The authors propose a plug-and-play feature alignment framework that learns a shared set of representative vectors via vector quantization. During inference, original features are mapped and fused to their nearest representative vectors, effectively suppressing domain-shift interference while preserving identity-discriminative information. The framework jointly optimizes the backbone network and representative vectors, incorporating consistency constraints and orthogonality regularization to construct a stable and generalizable embedding space. Extensive experiments demonstrate that the method significantly reduces equal error rates (EER) across multiple palmprint datasets and backbone architectures, substantially improving cross-domain generalization with minimal inference overhead.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• feature distribution shift
• domain mismatch
• open-set palmprint verification
• domain-invariant representation
• heterogeneous deployment conditions

Innovation

• feature alignment
• vector quantization
• open-set verification
• domain generalization
• plug-and-play framework