High-accurate and robust fingerprint anti-spoofing system using Optical Coherence Tomography

摘要

Traditional commercial automated fingerprint recognition systems (AFRSs) are vulnerable to fake attacks by the use of artificial fingerprints, due to its limitation on resolution and the difficulty of obtaining depth information. Developing new systems with strong anti-spoofing ability are of increasing concern in the current digital age. Inspired by the observation and comparison of fingertip skin structure and the one dimensional (1D) depth signals derived from OCT acquisition images between real finger and artificial fingerprints, two novel anti-spoofing features, namely depth-double-peak feature and sub-single-peak feature, were specifically defined. Depth-double-peak feature refers that there must be two and only two peaks in the 1D depth signal which reflects the double-peak structure of real fingertip skin. While sub single-peak feature means that there must be a peak in the ID depth signal intercepted before the maximum peak which distinguishes the extra layer covered on a real finger. Real and fake images could be presented by these two features and classified by a pre-learned threshold. Four types of artificial fingerprint, i.e., Normal Thickness Fingerprint Layer (NTFL), thin Fingerprint Layer (TFL), Ultra-Thin Fingerprint Layer (UTFL and Artificial Fingers Model (AFM) were collected to verify system robustness. 30 sets of real finger data (each set includes 400 images) and 60 sets of artificial fingerprint data (15 sets of each type) were collected by our designed OCT device. Experimental results show that our proposed anti-spoofing system could achieve 100% accuracy over all four types of artificial fingerprints and outperform the other automated anti-spoofing method in comparison. (C) 2019 Elsevier Ltd. All rights reserved.