Fingerprint is the most widely used biometric trait. Many factors may cause the quality degradation of fingerprint impressions: users, sensors and environmental facts. Most of the fingerprint-based biometric systems need an accurate prediction of fingerprint quality. A fingerprint quality measure can be used in enrollment or recognition stages, for improving the AFIS performances. In this work, a new fingerprint image quality estimation method guided by how experts classify fingerprint images quality is presented. By using six features, a continuous quality value is calculated. Experiments were performed in a well-known database. The proposed approach performance was evaluated by measuring its impact on the recognition stage and comparing it with the NFIQ quality algorithm. The Verifinger 4.2 was used as matching algorithm. The results shown that the proposed approach has a very good performance.
Fingerprints are the most widely used biometric characteristic. Due to the acquisition mode, fingerprint impressions can be classified into three classes: rolled, plain and latent. Latent fingerprint matching against rolled/plain fingerprints databases is a topic of great importance to law enforcement and forensics. This is the reason why maintaining consistency in the rolled fingerprints database has great importance. Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) are two extensively used techniques in data classification. In this article, a classification method of fingerprints into rolled and plain is proposed using SVM classifier. Three features are proposed to form the features vector due to its distinctive and discriminative characteristics. Our proposal achieved a classification accuracy of 99.1% using SVM, while with LDA the accuracy reached was 96.46%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.