M. Omidyeganeh, A. Javadtalab, S. Ghaemmaghami, S. Shirmohammadi
{"title":"基于鲁棒小波的指纹识别方法","authors":"M. Omidyeganeh, A. Javadtalab, S. Ghaemmaghami, S. Shirmohammadi","doi":"10.1109/ICMEW.2012.78","DOIUrl":null,"url":null,"abstract":"A robust fingerprint recognition system based on marginal statistics of 2D wavelet transform is introduced which significantly improves the accuracy of previous wavelet based approaches due to 1) a better selection of features extracted from the wavelet transform, and 2) a more accurate distance measure to find the similarity between fingerprints. A combination of Jain and Poincare algorithms is employed to locate the fingerprint reference point. The main part of the fingerprint image is chosen as a rectangle with the reference point at its center. The image is then divided into nonoverlapping sub-images, the wavelet transform is applied to the bi-level sub-images, and Generalized Gaussian Density (GGD) features are extracted from each wavelet sub band. Finally, the fingerprint recognition is done through the k-Nearest Neighbor (k-NN) classification employing Kullback-Leibler Distance (KLD) measure. Our test results confirm the superiority of the proposed method over the current fingerprint recognition methods.","PeriodicalId":385797,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A Robust Wavelet-based Approach to Fingerprint Indentification\",\"authors\":\"M. Omidyeganeh, A. Javadtalab, S. Ghaemmaghami, S. Shirmohammadi\",\"doi\":\"10.1109/ICMEW.2012.78\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A robust fingerprint recognition system based on marginal statistics of 2D wavelet transform is introduced which significantly improves the accuracy of previous wavelet based approaches due to 1) a better selection of features extracted from the wavelet transform, and 2) a more accurate distance measure to find the similarity between fingerprints. A combination of Jain and Poincare algorithms is employed to locate the fingerprint reference point. The main part of the fingerprint image is chosen as a rectangle with the reference point at its center. The image is then divided into nonoverlapping sub-images, the wavelet transform is applied to the bi-level sub-images, and Generalized Gaussian Density (GGD) features are extracted from each wavelet sub band. Finally, the fingerprint recognition is done through the k-Nearest Neighbor (k-NN) classification employing Kullback-Leibler Distance (KLD) measure. Our test results confirm the superiority of the proposed method over the current fingerprint recognition methods.\",\"PeriodicalId\":385797,\"journal\":{\"name\":\"2012 IEEE International Conference on Multimedia and Expo Workshops\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Multimedia and Expo Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMEW.2012.78\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Multimedia and Expo Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMEW.2012.78","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Robust Wavelet-based Approach to Fingerprint Indentification
A robust fingerprint recognition system based on marginal statistics of 2D wavelet transform is introduced which significantly improves the accuracy of previous wavelet based approaches due to 1) a better selection of features extracted from the wavelet transform, and 2) a more accurate distance measure to find the similarity between fingerprints. A combination of Jain and Poincare algorithms is employed to locate the fingerprint reference point. The main part of the fingerprint image is chosen as a rectangle with the reference point at its center. The image is then divided into nonoverlapping sub-images, the wavelet transform is applied to the bi-level sub-images, and Generalized Gaussian Density (GGD) features are extracted from each wavelet sub band. Finally, the fingerprint recognition is done through the k-Nearest Neighbor (k-NN) classification employing Kullback-Leibler Distance (KLD) measure. Our test results confirm the superiority of the proposed method over the current fingerprint recognition methods.