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

Abstract

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.

Wide band spectroscopic skin detection for contactless hand biometrics

“…Given that most of the previous proposals focused on fingerprint spoofing detection, we compare our proposal with three contact-based fingerprint aliveness detection approaches [15][16][17] and one [9] contactless one. The authors in [15,16] present approaches based on 2D imaging and fingerprint ridge-line pattern information. The performance achieved is less competitive but these approaches present the advantage of not needing a dedicated sensor for spoofing detection and that the recognition and the spoofing detection are made by using the same sensor.…”

On the importance of using high resolution images, third level features and sequence of images for fingerprint spoof detection

“…Fingerprint features can be classified into three levels: (i) Level 1: coarse features such as orientation field, (ii) Level 2: minutiae (ends or bifurcations of the ridges), and (iii) Level 3: micro level characteristics of the fingerprints, such as pores and dots. In the liveness detection problem, the interest in the level 3 features ([6, 7,8]) is due to the increased difficulty in faking such micro details in the spoof.…”

A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

“…Memon et al extended Manivanan's study by attempting to determine the optimum threshold value to detect the correlation peaks corresponding to active pores [Memon et al 2011;Memon 2012]. Espinoza and Champod [2011b] proposed a spoof detection method based on comparing pore quantity between spoof and live fingerprint images .…”