Classification of fingerprint images to real vs. spoof

Biometric identification is becoming a leading technology for identity management and security systems. Nonetheless, the use of counterfeit elastic fingerprints ('spoofing') may break these measures. In this paper, we address the problem of fingerprint spoofing based solely on image features extracted from 2D fingerprint images. By combining several low-accuracy methods, a robust high-performance classifier for real vs. fake fingerprint images is constructed. Its high accuracy is demonstrated on a large fingerprint database. The method thus shows high potential for improving existing fingerprint authentication devices.