Small Area Fingerprint Verification using Deep Convolutional Neural Network

Abstract

This paper presents a solution for a small area (partial-to-partial) fingerprint verification, which is suitable for mobile devices with small fingerprint sensors. Since images captured from the fingerprint sensor contain very limited minutiae features and less image overlapping areas between the input image and the template image, the minutiae-based method is hardly used. We propose a new approach that consists of two steps:(l) fingerprint alignment using band-limited phase-only correlation (BLPOC) and (2) fingerprint matching using deep convolutional neural network (DCNN). To handle the large displacement between two fingerprints, we added both preprocessing and post-processing steps to make the fingerprint alignment process more robust. The experiment results show that the proposed method achieves better performances compared to other methods.