{"title":"Fingerprint Recognition Based on Wavelet Transform and Ensemble Subspace Classifier","authors":"Andres Rojas, G. Dolecek","doi":"10.1109/urucon53396.2021.9647176","DOIUrl":null,"url":null,"abstract":"This paper presents a fingerprint recognition system based on Wavelet transform, multiple domain feature extraction, and Ensemble Subspace Discriminant Classifier. The main contribution of this work is the computation of a set of features that can be used for the classification of fingerprints, and the implementation of an ensemble of discriminant classifiers. First, the review of previous works is presented. Next, a detailed description of the proposed method is elaborated. Finally, it is shown that the proposed system provides the highest accuracy (97.5%) in comparison with other works proposed in the literature using a different type of classifier such as the ensemble subspace discriminant.","PeriodicalId":337257,"journal":{"name":"2021 IEEE URUCON","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE URUCON","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/urucon53396.2021.9647176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a fingerprint recognition system based on Wavelet transform, multiple domain feature extraction, and Ensemble Subspace Discriminant Classifier. The main contribution of this work is the computation of a set of features that can be used for the classification of fingerprints, and the implementation of an ensemble of discriminant classifiers. First, the review of previous works is presented. Next, a detailed description of the proposed method is elaborated. Finally, it is shown that the proposed system provides the highest accuracy (97.5%) in comparison with other works proposed in the literature using a different type of classifier such as the ensemble subspace discriminant.