{"title":"基于光学衍射场的掌纹识别","authors":"Qing Xiao;Yixuan Wu;Shaohua Tao","doi":"10.1109/JSEN.2025.3577623","DOIUrl":null,"url":null,"abstract":"Palmprint recognition is critical for high-security applications, such as access control and forensic investigations, due to its rich information content and resistance to forgery. However, extracting reliable features from low-quality palmprints, common in real-world scenarios like latent prints at crime scenes, remains challenging. Recent high-resolution palmprint research has focused on image preprocessing and feature extraction; however, errors introduced during preprocessing can compromise feature reliability, thereby degrading recognition accuracy. In this article, we propose an optical diffraction field-based method that extracts frequency-domain features from the grating-like ridge patterns of palmprints. Feature matching is evaluated using similarity measures including structural similarity index, Pearson correlation coefficient, and cosine similarity, with a random forest classifier for decision fusion. This method simplifies preprocessing, reduces computational complexity, and enhances robustness against noise and deformations. Experimental results on the THUPLMLAB dataset (a publicly available high-resolution palmprint database) achieve an equal error rate (EER) of 1.00%, demonstrating competitive performance against state-of-the-art methods reliant on intensive preprocessing. The proposed method provides a physically interpretable, efficient, and robust solution for biometric palmprint recognition.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"27230-27237"},"PeriodicalIF":4.3000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optical Diffraction Field-Based Palmprint Recognition\",\"authors\":\"Qing Xiao;Yixuan Wu;Shaohua Tao\",\"doi\":\"10.1109/JSEN.2025.3577623\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Palmprint recognition is critical for high-security applications, such as access control and forensic investigations, due to its rich information content and resistance to forgery. However, extracting reliable features from low-quality palmprints, common in real-world scenarios like latent prints at crime scenes, remains challenging. Recent high-resolution palmprint research has focused on image preprocessing and feature extraction; however, errors introduced during preprocessing can compromise feature reliability, thereby degrading recognition accuracy. In this article, we propose an optical diffraction field-based method that extracts frequency-domain features from the grating-like ridge patterns of palmprints. Feature matching is evaluated using similarity measures including structural similarity index, Pearson correlation coefficient, and cosine similarity, with a random forest classifier for decision fusion. This method simplifies preprocessing, reduces computational complexity, and enhances robustness against noise and deformations. Experimental results on the THUPLMLAB dataset (a publicly available high-resolution palmprint database) achieve an equal error rate (EER) of 1.00%, demonstrating competitive performance against state-of-the-art methods reliant on intensive preprocessing. The proposed method provides a physically interpretable, efficient, and robust solution for biometric palmprint recognition.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 14\",\"pages\":\"27230-27237\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11036602/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/11036602/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Palmprint recognition is critical for high-security applications, such as access control and forensic investigations, due to its rich information content and resistance to forgery. However, extracting reliable features from low-quality palmprints, common in real-world scenarios like latent prints at crime scenes, remains challenging. Recent high-resolution palmprint research has focused on image preprocessing and feature extraction; however, errors introduced during preprocessing can compromise feature reliability, thereby degrading recognition accuracy. In this article, we propose an optical diffraction field-based method that extracts frequency-domain features from the grating-like ridge patterns of palmprints. Feature matching is evaluated using similarity measures including structural similarity index, Pearson correlation coefficient, and cosine similarity, with a random forest classifier for decision fusion. This method simplifies preprocessing, reduces computational complexity, and enhances robustness against noise and deformations. Experimental results on the THUPLMLAB dataset (a publicly available high-resolution palmprint database) achieve an equal error rate (EER) of 1.00%, demonstrating competitive performance against state-of-the-art methods reliant on intensive preprocessing. The proposed method provides a physically interpretable, efficient, and robust solution for biometric palmprint recognition.
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