{"title":"An optimized fingerprint matcher","authors":"Shuvra Chakraborty","doi":"10.1109/ICIINFS.2011.6038063","DOIUrl":null,"url":null,"abstract":"This paper presents a fingerprint matching system which uses eight directional Gabor filter bank, a popular method for enhancing poor quality image, to capture global and local information available in the fingerprints. A new region of interest has been experimented for feature vector compaction. Here, feature vectors are extracted from the directional representation of enhanced image. Matching is extremely fast as it computes only Euclidian difference between feature vectors to compute matching score. Feature vector requires least memory as compared to traditional minutiae based approach as it stores only 64 intensity values. This filter-bank approach has been tested on 800 images of DB1_a of FVC 2002 and 77.125% images are accepted correctly.","PeriodicalId":353966,"journal":{"name":"2011 6th International Conference on Industrial and Information Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 6th International Conference on Industrial and Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIINFS.2011.6038063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper presents a fingerprint matching system which uses eight directional Gabor filter bank, a popular method for enhancing poor quality image, to capture global and local information available in the fingerprints. A new region of interest has been experimented for feature vector compaction. Here, feature vectors are extracted from the directional representation of enhanced image. Matching is extremely fast as it computes only Euclidian difference between feature vectors to compute matching score. Feature vector requires least memory as compared to traditional minutiae based approach as it stores only 64 intensity values. This filter-bank approach has been tested on 800 images of DB1_a of FVC 2002 and 77.125% images are accepted correctly.
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