Fingerprint Segmentation via Convolutional Neural Networks

Abstract

In automatic fingerprint identification systems, it is crucial to segment the fingerprint images. Inspired by the superiority of convolutional neural networks for various classification and regression tasks, we approach fingerprint segmentation as a binary classification problem and propose a convolutional neural network based method for fingerprint segmentation. Given a fingerprint image, we first apply the total variation model to decompose it into cartoon and texture components. Then, the obtained texture component image is divided into overlapping patches, which are classified by the trained convolutional neural network as either foreground or background. Based on the classification results and by applying morphology-based post-processing, we get the final segmentation result for the whole fingerprint image. In the experiments, we investigate the effect of different patch sizes on the segmentation performance, and compare the proposed method with state-of-the-art algorithms on FVC2000, FVC2002 and FVC2004. Experimental results demonstrate that the proposed method outperforms existing algorithms.