  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.
