One-class SVM with negative examples for fingerprint liveness detection

Abstract

The study of the artificial fingerprint detection has lasted for a decade. With full prior knowledge of the spoof attack, researchers extract discriminative features and apply some two-class classifiers to detect the spoof. However, we don't know the materials of fake fingerprints used by the attackers in the real world. It means that the traditional evaluation is not scientific. In this paper, we proposed to measure the security of fake fingerprint detection systems by the inter-operability performance across various materials. Fake fingerprints made of various materials have diverse feature distributions. The traditional binary SVM over-fits the training negative data. We proposed a novel model named one-class SVM with negative examples (OCSNE) to solve the problem. In order to simulate the real environment, we modified the structure of the Liveness Detection Competition 2011 (LivDet2011) database accordingly. The experimental results on the LivDet2011 modified database showed OCSNE outperforms the traditional SVM.