Vitality Detection from Fingerprint Images: A Critical Survey

Coli, Pietro; Marcialis, Gian Luca; Roli, Fabio

doi:10.1007/978-3-540-74549-5_76

Cited by 50 publications

(40 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to understand the motivation of organizing 80 such a competition, we observed that the first trials to face with this topic were often carried out with home-made data sets that were not publicly available, experimental protocols were not unique, and the same reported results were obtained on very small data sets. We pointed out these issues in [7].…”

mentioning

confidence: 95%

Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015

Ghiani

Yambay

Mura

et al. 2017

Image and Vision Computing

Self Cite

108

View full text Add to dashboard Cite

A spoof attack, a subset of presentation attacks, is the use of an artificial replica of a biometric in an attempt to circumvent a biometric sensor. Liveness detection, or presentation attack detection, distinguishes between live and fake biometric traits and is based on the principle that additional information can be garnered above and beyond the data procured by a standard authentication system to determine if a biometric measure is authentic. There has been a noticeable increase in the number of participants in LivDet competitions as well as a noticeable decrease in error rates across competitions.

show abstract

mentioning

confidence: 95%

Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015

Ghiani

Yambay

Mura

et al. 2017

Image and Vision Computing

Self Cite

108

View full text Add to dashboard Cite

show abstract

“…The Fourier transform feature can quantify the difference in terms of high-frequency information loss for fake fingers. This approach is tested for a single scanner and silicon spoof material with average spoof/live EER of 2.4 % on a dataset of 720 fake and 720 live images from 36 individuals [6] and for gelatin and silicon with an average of 23 % EER for a dataset of 900 fake and 450 live images from 30 individuals [20].…”

Section: Characteristics Of Spoof and Live Imagesmentioning

confidence: 99%

“…In 2007 [6], a taxonomy is presented whereby methods are divided into software and hardware based. A similar division is suggested, but also consider an additional category where liveness is inherent to the biometric, i.e., it must be present in order to capture the biometric [2].…”

mentioning

confidence: 99%

Anti-spoofing: Fingerprint (hardware and software)

Schuckers¹

2014

Encyclopedia of Biometrics

View full text Add to dashboard Cite

DefinitionIn biometric systems, the goal of liveness testing is to determine if the biometric being captured is an actual measurement from the authorized, live person who is present at the time of capture. While fingerprint systems may have an excellent performance and improve security, previous studies have shown it is not difficult to make molds of latent fingerprints left by legitimate users and to create fake fingers made from Play-Doh, gelatin, and silicon materials to fool a variety of fingerprint scanners, termed spoofing. Liveness detection reduces the risk of spoofing by requiring a liveness signature, in addition to matched biometric information. Methods can be divided into hardware and software categories. Hardware methods include measurements like pulse oximetry, electrocardiogram, or odor, while software-based measurements use additional processing of the biometric information itself to isolate liveness signatures like perspiration and deformation. While liveness algorithm makes spoofing more difficult, they need to be considered as components of a biometric system, which bring with it performance characteristics along with factors such as ease of use, collectability, universality, spoof-ability, permanence, and in some cases, even uniqueness. No system is perfect in its ability to prevent spoof attacks. However, liveness algorithms can reduce this vulnerability to minimize the risk of spoofing.Fingerprints are graphical ridge-valley patterns from human fingers. Fingerprint recognition is a widely used and efficient technique for biometric authentication. While fingerprint systems may have excellent performance and improve security, previous studies have shown it is not difficult to make molds of latent fingerprints left by legitimate users and to create fake fingers made from Play-Doh, gelatin, and silicon materials to fool a variety of fingerprint scanners [1,2]. The most famous of which is the work by Matsumoto and colleagues. In the reports, two different techniques were used to create a mold. The first technique directly used a subject's finger to create the mold in free molding plastic, whereas the second technique involved making a mold from a latent fingerprint image. Casts were made of gelatin material and termed "gummy fingers." Verification rates of gummy fingers ranged from 68 to 100 %. For method of creating a cast from residual fingerprints, all fingerprint systems were able to enroll the spoof finger and verify more than 67 % of the attempts. Similar results have been obtained on subsequent studies with various materials including silicon, clay, and Play-Doh [1, 2] and on one study which looked at cadaver fingers [2].

show abstract

“…Hardware-and software-based algorithms for the so-called fingerprint liveness detection have been already developed. The first survey on this topic is [8]. Several other approaches have been subsequently proposed [9], [10].…”

Section: Introductionmentioning

confidence: 97%

Robustness analysis of likelihood ratio score fusion rule for multimodal biometric systems under spoof attacks

Akhtar

Fumera

Marcialis

et al. 2011

2011 Carnahan Conference on Security Technology

Self Cite

View full text Add to dashboard Cite

Recent works have shown that, contrary to a common belief, multi-modal biometric systems may be "forced" by an impostor by submitting a spoofed biometric replica of a genuine user to only one of the matchers. Although those results were obtained under a worst-case scenario when the attacker is able to replicate the exact appearance of the true biometric, this raises the issue of investigating more thoroughly the robustness of multimodal systems against spoof attacks and devising new methods to design robust systems against them. To this aim, in this paper we propose a robustness evaluation method which takes into account also scenarios more realistic than the worst-case one. Our method is based on an analytical model of the score distribution of fake traits, which is assumed to lie between the one of genuine and impostor scores, and is parametrised by a measure of the relative distance to the distribution of impostor scores, we name "fake strength". Varying the value of such parameter allows one to simulate the different factors which can affect the distribution of fake scores, like the ability of the attacker to replicate a certain biometric. Preliminary experimental results on real bimodal biometric data sets made up of faces and fingerprints show that the widely used LLR rule can be highly vulnerable to spoof attacks against one only matcher, even when the attack has a low fake strength.

show abstract

Vitality Detection from Fingerprint Images: A Critical Survey

Cited by 50 publications

References 14 publications

Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015

Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015

Anti-spoofing: Fingerprint (hardware and software)

Robustness analysis of likelihood ratio score fusion rule for multimodal biometric systems under spoof attacks

Contact Info

Product

Resources

About