{"title":"CRCGAN: Toward robust feature extraction in finger vein recognition","authors":"","doi":"10.1016/j.patcog.2024.111064","DOIUrl":null,"url":null,"abstract":"<div><div>Deep convolutional neural networks (CNNs) have produced remarkable outcomes in finger vein recognition. However, these networks often overfit label information, losing essential image features, and are sensitive to noise, with minor input changes leading to incorrect recognition. To address above problems, this paper presents a new classification reconstruction cycle generative adversarial network (CRCGAN) for finger vein recognition. CRCGAN comprises a feature generator, a feature discriminator, an image generator, and an image discriminator, which are designed for robust feature extraction. Concretely, the feature generator extracts features for classification, while the image generator reconstructs images from these features. Two discriminators provide feedback, guiding the generators to improve the quality of generated data. With this design of bi-directional image-to-feature mapping and cyclic adversarial training, CRCGAN achieves the extraction of essential features and minimizes overfitting. Additionally, precisely due to the extraction of essential features, CRCGAN is not sensitive to noise. Experimental results on three public databases, including THU-FVFDT2, HKPU, and USM, demonstrate CRCGAN’s competitive performance and strong noise resistance, achieving recognition accuracies of 98.36%, 99.17% and 99.49% respectively, with less than 0.5% degradation on HKPU and USM databases under noisy conditions.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-04","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/S003132032400815X","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
Deep convolutional neural networks (CNNs) have produced remarkable outcomes in finger vein recognition. However, these networks often overfit label information, losing essential image features, and are sensitive to noise, with minor input changes leading to incorrect recognition. To address above problems, this paper presents a new classification reconstruction cycle generative adversarial network (CRCGAN) for finger vein recognition. CRCGAN comprises a feature generator, a feature discriminator, an image generator, and an image discriminator, which are designed for robust feature extraction. Concretely, the feature generator extracts features for classification, while the image generator reconstructs images from these features. Two discriminators provide feedback, guiding the generators to improve the quality of generated data. With this design of bi-directional image-to-feature mapping and cyclic adversarial training, CRCGAN achieves the extraction of essential features and minimizes overfitting. Additionally, precisely due to the extraction of essential features, CRCGAN is not sensitive to noise. Experimental results on three public databases, including THU-FVFDT2, HKPU, and USM, demonstrate CRCGAN’s competitive performance and strong noise resistance, achieving recognition accuracies of 98.36%, 99.17% and 99.49% respectively, with less than 0.5% degradation on HKPU and USM databases under noisy conditions.
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.