LivDet-iris 2013 - Iris Liveness Detection Competition 2013

Abstract

The use of an artificial replica of a biometric characteristic in an attempt to circumvent a system is an example of a biometric presentation attack. Liveness detection is one of the proposed countermeasures, and has been widely implemented in fingerprint and iris recognition systems in recent years to reduce the consequences of spoof attacks. The goal for the Liveness Detection (LivDet) competitions is to compare software-based iris liveness detection methodologies using a standardized testing protocol and large quantities of spoof and live images. Three submissions were received for the competition Part 1The best results from across all three datasets was from Federico with a rate of falsely rejected live samples of 28.6% and the rate of falsely accepted fake samples of 5.7%.

Presentation attack detection using Laplacian decomposed frequency response for visible spectrum and Near-Infra-Red iris systems

“…The classification output is considered for various frames and the final decision for the entire video is made using majority voting of classification scores obtained on individual frames. As the proposed algorithm is based on frequency localization using Laplacian Pyramids at different scales, we evaluate the robustness of the proposed algorithm to detect the presentation attack in NIR domain also by employing wellknown Warsaw iris dataset from LiveDet 2013 competition [24,3]. The proposed algorithm has indicated its agnostic nature that is independent of the visible spectrum and NIR domain.…”

“…A presentation attacks, which is also widely called as spoof attack, is the process of attacking the biometric capture device by presenting the biometric artefacts to gain access in a secure authentication scenario. Many studies such as [5,9,7,16,10,24,3], have investigated presentation attacks on NIR based iris recognition systems specifically for printed artefact presentation at the sensor level.…”

Contact lens detection in iris images

“…For instance, the results of Iris Liveness Detection Competition 2013 (LivDet-Iris 2013) [57] demonstrate that cosmetic lenses are indeed much more difficult to detect compared to iris paper printouts. One reason for this is that the artefact is visible only within a very small part of the iris image, whereas usually the whole periocular region corresponds to the artefact in the case of a print attack.…”

“…The benchmark datasets contain usually separate folds only for training and testing which may cause bias due to "data peeking". While independent (third-party) evaluations are impossible to arrange without collective evaluations, like LivDet-Iris 2013 [57], the use of pre-defined training, development and test sets would mitigate the effect of tuning the methods on the test data. Unambiguous evaluation protocols would also allow fairer and direct comparison between different studies.…”

“…However, the almost perfect CCRs for textured contact lens detection in "homogeneous" training and test conditions indicate that even more challenging configurations are needed. On the other hand, LivDet-Iris 2013 competition [57] showed that already these kinds of experimental setups can be made challenging if the actual test data is kept inaccessible during algorithm development and third-party evaluation is deployed in order to simulate real-world operating conditions. The resulting performances were far from satisfactory even for the winning method, which suggest that the laboratory results reported in scientific papers might be indeed overly optimistic estimate on the true generalisation capabilities of the existing textured contact lens detection methods.…”