Congcong Jia , Xingbo Dong , Yen Lung Lai , Andrew Beng Jin Teoh , Ziyuan Yang , Xiaoyan Zhang , Liwen Wang , Zhe Jin , Lianqiang Yang
{"title":"Single source domain generalization for palm biometrics","authors":"Congcong Jia , Xingbo Dong , Yen Lung Lai , Andrew Beng Jin Teoh , Ziyuan Yang , Xiaoyan Zhang , Liwen Wang , Zhe Jin , Lianqiang Yang","doi":"10.1016/j.patcog.2025.111620","DOIUrl":null,"url":null,"abstract":"<div><div>In 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 <span><span>https://github.com/yocii/PalmRSS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"165 ","pages":"Article 111620"},"PeriodicalIF":7.5000,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320325002808","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In 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.
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.