X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication

2026-06-06

AI Summary

This study addresses the significant domain gap between controlled palmprint acquisition and unconstrained smartphone-based authentication, a challenge inadequately covered by existing datasets due to their limited representation of real-world composite variations. To bridge this gap, the authors propose a paired-identity collection paradigm and introduce X-Palm, a cross-domain palmprint dataset comprising 6,006 images from 103 subjects, simultaneously captured under controlled multispectral scanning and user-initiated smartphone imaging conditions. The dataset systematically incorporates realistic variations in hardware, pose, illumination, and background. Benchmark evaluations using twelve state-of-the-art models reveal a substantial performance drop on X-Palm, highlighting the difficulty of cross-domain generalization. However, models trained on this dataset demonstrate markedly improved robustness, effectively advancing palmprint recognition toward practical deployment.

Abstract

Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and unconstrained authentication. Existing datasets are largely restricted to controlled setups and fail to capture the compound variability of real-world environments. In this paper, we introduce X-Palm, a cross-domain dataset comprising 6,006 palm images from 103 individuals (206 hands). To the best of our knowledge, X-Palm is the first palmprint dataset providing novel paired-identity acquisition specifically designed to bridge the gap between reliably controlled multispectral enrollment and unconstrained mobile authentication while encompassing a broad spectrum of in-the-wild variability. Unlike existing datasets that focus on single to a few variations, X-Palm addresses the massive modality and environmental shifts encountered in practical deployments by capturing paired data for identities across two distinct domains: (1) a controlled Multispectral Palmprint setting using our custom-developed scanner, and (2) an unconstrained smartphone palmprint setting that is participant-driven, incorporating simultaneous variations in hardware, hand pose, illumination, background, camera-to-hand distance, perspective, and palm surface conditions (e.g., moisture and occlusions). Our extensive benchmarks of 12 SOTA models reveal that while existing methods achieve high performance on controlled data, they experience severe performance collapse on X-Palm. Conversely, models trained on X-Palm demonstrate consistent robustness across domains, positioning X-Palm as a valuable resource for training a model towards real-world, cross-domain generalization. Data access instructions and the related benchmarking codes are publicly available at: https://github.com/X-Palm/X-Palm-2026

Problem

• palmprint authentication
• cross-domain
• domain gap
• unconstrained environment
• biometric recognition

Innovation

• cross-domain palmprint
• paired multispectral-smartphone dataset
• unconstrained authentication
• domain gap
• in-the-wild variability