Risk evaluation for spoofing against a sensor supplied with liveness detection

Espinoza, Marcela; Champod, Christophe

doi:10.1016/j.forsciint.2010.05.025

Cited by 17 publications

(17 citation statements)

References 33 publications

(34 reference statements)

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“…However, the quality of the reproduced fingerprint image may be affected by the confluence of different factors such as the initial pressure of the finger on the cast and the contact of the stamp on the sensor acquisition surface, which may alter the fingerprint shape [Coli et al 2007b]. Furthermore, rates of successful spoof attacks are influenced by the nature of both the mold and the spoof [Espinoza and Champod 2011a]. First, the mold material impacts the quality of the spoof; some molds are more amenable for reproducing the fingerprint pattern than others.…”

Section: Noncooperative Duplicationmentioning

confidence: 99%

A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

2014

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Several issues related to the vulnerability of fingerprint recognition systems to attacks have been highlighted in the biometrics literature. One such vulnerability involves the use of artificial fingers, where materials such as Play-Doh, silicone, and gelatin are inscribed with fingerprint ridges. Researchers have demonstrated that some commercial fingerprint recognition systems can be deceived when these artificial fingers are placed on the sensor; that is, the system successfully processes the ensuing fingerprint images, thereby allowing an adversary to spoof the fingerprints of another individual. However, at the same time, several countermeasures that discriminate between live fingerprints and spoof artifacts have been proposed. While some of these antispoofing schemes are hardware based, several software-based approaches have been proposed as well. In this article, we review the literature and present the state of the art in fingerprint antispoofing. ACM Reference Format:Emanuela Marasco and Arun Ross. 2014. A survey on antispoofing schemes for fingerprint recognition systems.

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Section: Noncooperative Duplicationmentioning

confidence: 99%

A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

2014

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“…With a swipe sensor (a tight line of sensors), the client slides a finger vertically over the surface. These sensors are ideally utilized as a part of versatile customer electronics because of their size and shape [3,4].…”

Section: Finger Print Sensing Technologiesmentioning

confidence: 99%

“…In [4], Umut Uludag, Anil K. Jain. Attacks on Biometric Systems: A Case Study in Fingerprints tested several fingerprint sensors to check whether they accept an artificially created (dummy) finger instead of a real finger.…”

Section: The Paper Introduces By Hong Et Al Entitle With a New Appromentioning

confidence: 99%

Survey on Fingerprint Recognition System using Image Preprocessing and De-noising

Khard¹,

Baraskar²,

Chaurasia³

2016

IJCA

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Biometric authentication plays a major role in security as these are by nature unique for every human. But the security is compromised when the pattern matching system is not accurate. Authentication system like fingerprint recognition is most commonly used biometric authentication system. In this paper survey is done on fingerprint recognition techniques. And different approaches are studied in terms of accuracy and performance. As fingerprint may also contain noise; so image de-noising techniques are also studied Cross ridge frequency analysis of fingerprint images is performed by means of statistical measures and weighted mean phase is calculated. These different features along with ridge reliability or ridge centre frequency are given as inputs to a fuzzy c-means classifier..

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“…The same is true for the risks associated with this technology. The vulnerability to attacks directed against the sensor, such as spoofing attacks, constitutes an important threat [2][3][4][5]. A spoofing attack consists in using a fake to impersonate a legitimate user.…”

Section: Introductionmentioning

confidence: 99%

“…By fake, we mean a tridimensional reproduction of the friction ridge skin portion of a genuine fingertip. These reproductions can be produced by either using a cast of the original finger or from a fingermark left by the legitimate user [2,[4][5]. The term of "direct production" is commonly used to designate the first situation, for which the finger is directly pressed on a soft material in order to produce the cast.…”

Section: Introductionmentioning

confidence: 99%

Using the Number of Pores on Fingerprint Images to Detect Spoofing Attacks

Espinoza

Champod

2011

2011 International Conference on Hand-Based Biometrics

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Due to the growing use of biometric technologies in our modern society, spoofing attacks are becoming a serious concern. Many solutions have been proposed to detect the use of fake "fingerprints" on an acquisition device. In this paper, we propose to take advantage of intrinsic features of friction ridge skin: pores. The aim of this study is to investigate the potential of using pores to detect spoofing attacks.Results show that the use of pores is a promising approach. Four major observations were made: First, results confirmed that the reproduction of pores on fake "fingerprints" is possible. Second, the distribution of the total number of pores between fake and genuine fingerprints cannot be discriminated. Third, the difference in pore quantities between a query image and a reference image (genuine or fake) can be used as a discriminating factor in a linear discriminant analysis. In our sample, the observed error rates were as follows: 45.5% of false positive (the fake passed the test) and 3.8% of false negative (a genuine print has been rejected). Finally, the performance is improved by using the difference of pore quantity obtained between a distorted query fingerprint and a non-distorted reference fingerprint. By using this approach, the error rates improved to 21.2% of false acceptation rate and 8.3% of false rejection rate.

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Risk evaluation for spoofing against a sensor supplied with liveness detection

Cited by 17 publications

References 33 publications

A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

Survey on Fingerprint Recognition System using Image Preprocessing and De-noising

Using the Number of Pores on Fingerprint Images to Detect Spoofing Attacks

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