Ridouane Oulhiq, Saad Ibntahir, Marouane Sebgui, Z. Guennoun
{"title":"A fingerprint recognition framework using Artificial Neural Network","authors":"Ridouane Oulhiq, Saad Ibntahir, Marouane Sebgui, Z. Guennoun","doi":"10.1109/SITA.2015.7358382","DOIUrl":null,"url":null,"abstract":"Fingerprinting is one of the most used biometrics for people identification, it relays on image processing and classification algorithms. In this work we propose and test a framework that enables fingerprint detection using a set of image pre-processing algorithm. Concerning the features extraction, we propose the use of the number of bifurcations in image localities, and we propose the use of Artificial Neural Network (ANN) for the classification. The performance of our framework is evaluated for three different activation functions and show that we can reach an accuracy of 81%.","PeriodicalId":174405,"journal":{"name":"2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITA.2015.7358382","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Fingerprinting is one of the most used biometrics for people identification, it relays on image processing and classification algorithms. In this work we propose and test a framework that enables fingerprint detection using a set of image pre-processing algorithm. Concerning the features extraction, we propose the use of the number of bifurcations in image localities, and we propose the use of Artificial Neural Network (ANN) for the classification. The performance of our framework is evaluated for three different activation functions and show that we can reach an accuracy of 81%.