Iris Liveness Detection using a Cascade of Dedicated Deep Learning Networks

• URL: http://arxiv.org/abs/2105.14009v1
• Date: Fri, 28 May 2021 17:37:11 GMT
• Title: Iris Liveness Detection using a Cascade of Dedicated Deep Learning Networks
• Abstract summary: Presentation attacks can bypass biometric authentication systems using printed images, artificial eyes, textured contact lenses, etc. This paper proposes a serial architecture based on a MobileNetV2 modification, trained from scratch to classify bona fide iris images versus presentation attack images.
• Score: 11.617910256604143
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Iris pattern recognition has significantly improved the biometric authentication field due to its high stability and uniqueness. Such physical characteristics have played an essential role in security and other related areas. However, presentation attacks, also known as spoofing techniques, can bypass biometric authentication systems using artefacts such as printed images, artificial eyes, textured contact lenses, etc. Many liveness detection methods that improve the security of these systems have been proposed. The first International Iris Liveness Detection competition, where the effectiveness of liveness detection methods is evaluated, was first launched in 2013, and its latest iteration was held in 2020. This paper proposes a serial architecture based on a MobileNetV2 modification, trained from scratch to classify bona fide iris images versus presentation attack images. The bona fide class consists of live iris images, whereas the attack presentation instrument classes are comprised of cadaver, printed, and contact lenses images, for a total of four scenarios. All the images were pre-processed and weighted per class to present a fair evaluation. This proposal won the LivDet-Iris 2020 competition using two-class scenarios. Additionally, we present new three-class and four-class scenarios that further improve the competition results. This approach is primarily focused in detecting the bona fide class over improving the detection of presentation attack instruments. For the two, three, and four classes scenarios, an Equal Error Rate (EER) of 4.04\%, 0.33\%, and 4,53\% was obtained respectively. Overall, the best serial model proposed, using three scenarios, reached an ERR of 0.33\% with an Attack Presentation Classification Error Rate (APCER) of 0.0100 and a Bona Fide Classification Error Rate (BPCER) of 0.000. This work outperforms the LivDet-Iris 2020 competition results.

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