Congcong JiaXingbo DongYen Lung LaiAndrew Beng Jin TeohZiyuan YangXiaoyan ZhangLiwen WangZhe JinLianqiang Yang2025-10-282025-10-282025-0910.1016/j.patcog.2025.111620https://dspace-cris.utar.edu.my/handle/123456789/11615In palmprint recognition, domain shifts caused by device differences and environmental variations presents a significant challenge. Existing approaches often require multiple source domains for effective domain generalization (DG), limiting their applicability in single-source domain scenarios. To address this challenge, we propose PalmRSS, a novel Palm Recognition approach based on Single Source Domain Generalization (SSDG). PalmRSS reframes the SSDG problem as a DG problem by partitioning the source domain dataset into subsets and employing image alignment and adversarial training. PalmRSS exchanges low-level frequencies of palm data and performs histogram matching between samples to align spectral characteristics and pixel intensity distributions. Experiments demonstrate that PalmRSS outperforms state-of-the-art methods, highlighting its effectiveness in single source domain generalization. The code is released at https://github.com/yocii/PalmRSS. © 2025 Elsevier LtdenHistogram matchingLow-level frequenciesOpen-set recognitionPalmprint recognitionSingle source domain generalizationEffective domainsEnvironmental variationsGeneralisationLow-level frequencyMultiple sourcePalmprintsSingle sourcejournal-article