Rolled-plain fingerprint images classification

Abstract

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%.