Fingerprint recognition systems are vulnerable to artificial spoof fingerprint attacks, like molds made of silicone, gelatin or Play-Doh. "Liveness detection", which is to detect vitality information from the biometric signature itself, has been proposed to defeat these kinds of spoof attacks. The goal for the LivDet 2009 competition is to compare different methodologies for softwarebased fingerprint liveness detection with a common experimental protocol and large dataset of spoof and live images. This competition is open to all academic and industrial institutions which have a solution for software-based fingerprint vitality detection problem. Four submissions resulted in successful completion: Dermalog, ATVS, and two anonymous participants (one industrial and one academic). Each participant submitted an algorithm as a Win32 console application. The performance was evaluated for three datasets, from three different optical scanners, each with over 1500 images of "fake" and over 1500 images of "live" fingerprints. The best results were from the algorithm submitted by Dermalog with a performance of 2.7% FRR and 2.8% FAR for the Identix (L-1) dataset. The competition goal is to become a reference event for academic and industrial research in software-based fingerprint liveness detection and to raise the visibility of this important research area in order to decrease risk of fingerprint systems to spoof attacks.

Abstract. Although fingerprint verification systems reached a high degree of accuracy, it has been recently shown that they can be circumvented by "fake fingers", namely, fingerprint images coming from stamps reproducing an user fingerprint, which is processed as an "alive" one. Several methods have been proposed for facing with this problem, but the issue is far from a final solution. Since the problem is relevant both for the academic and the industrial communities, in this paper, we present a critical review of current approaches to fingerprint vitality detection in order to analyze the state-of-the art and the related open issues.

Despite its importance, a few works have been proposed for fingerprint vitality detection. In this paper, we propose a novel feature for detecting the "liveness" of fingerprint images. This feature is derived from the image power spectrum, and point out the difference between "live " and "fake" images in terms of high frequency information loss. Preliminary results on a large data set show the effectiveness of the proposed measure

The automatic vitality detection of a fingerprint has become an important issue in personal verification systems based on this biometric. It has been shown that fake fingerprints made using materials like gelatine or silicon can deceive commonly used sensors. Recently, the extraction of vitality features from fingerprint images has been proposed to address this problem. Among others, static and dynamic features have been separately studied so far, thus their respective merits are not yet clear; especially because reported results were often obtained with different sensors and using small data sets which could have obscured relative merits, due to the potential small sample-size issues. In this paper, we compare some static and dynamic features by experiments on a larger data set and using the same optical sensor for the extraction of both feature sets. We dealt with fingerprint stamps made using liquid silicon rubber. Reported results show the relative merits of static and dynamic features and the performance improvement achievable by using such features together.

Although fingerprint verification systems have attained a good performance, researchers recently pointed out their weakness under fraudulent attacks by fake fingers. In fact, the acquisition sensor can be deceived by fake fingerprints created with liquid silicon rubber. Among the solutions to this problem, the software-based ones are the cheapest and less intrusive. They use feature vectors made up of measures extracted from one or multiple impressions (static measures) or multiple frames (dynamic measures) of the same finger in order to distinguish live and fake fingers. In this paper, we jointly use both static and dynamic features and report an experimental investigation aimed to compare them and select the most effective ones.