Aliveness Detection for IRIS Biometrics

Abstract

Various experiments show an alarming lack of anti-spoofing mechanisms in devices already protecting many sensitive areas all over the world, proving that aliveness detection methods must be quickly included in commercial equipment. To introduce and systemize the topic, the paper begins with a survey of possible types of eye forgery, together with possible countermeasures. The authors introduce three solutions of eye aliveness detection, based on analyses of image frequency spectrum, controlled light reflection from the cornea, and pupil dynamics. A body of various fake (printed) eye images was used to test the developed methodologies, including different printers and printout carriers. The proposed methodology was embedded into the NASK iris recognition system and showed its large potential. For a local database of pairs of alive and printed eyes, all methods proposed in the paper revealed zero False Acceptance Rate of Fakes FAR-F. The False Rejection Rate of Genuines FRR-G reached 2.8% for the first proposed solution, and showed null value for the next two proposed methods. This very favorable compares to the commercial equipment tested: two popular iris cameras accepted 73% and 15% of the prepared fake irises.

Hill-Climbing Attack Based on the Uphill Simplex Algorithm and Its Application to Signature Verification

“…Several works have already studied the robustness of biometric systems against direct attacks, specially fingerprint-and iris-based, including [2,3,6]. In the case of indirect attacks, most of the studies use some kind of variant of the hillclimbing algorithm [4].…”

“…There are two main types of attacks that may put at risk the security offered by a biometric system: (i) direct attacks, carried out against the sensor using synthetic traits, such as printed iris images or gummy fingers [2]; and (ii) indirect attacks, carried out against some of the inner modules of the system [3,4], and thus requiring for the attacker to have some knowledge about the system (e.g., storage format or matcher used). A more detailed analysis of the vulnerable points of biometric systems is made by Ratha et al in [5].…”

A high performance fingerprint liveness detection method based on quality related features

“…Different liveness detection algorithms have been proposed for traits such as fingerprint [20][21][22], face [23][24][25], or iris [26][27][28]. These algorithms can broadly be divided into:…”

“…In this case some specific device is added to the sensor in order to detect particular properties of a living trait such as the blood pressure [29], the odor [30], or the pupil hippus [27].…”

LBP − TOP Based Countermeasure against Face Spoofing Attacks

“…[23], [14] and [18] indicate that fingerprint authentication systems suffer from similar weakness. [11], [12] and [19] diagnose the same shortcoming on iris recognition systems. Finally, [5] and [7] address spoofing attacks to speaker biometrics.…”