  The iris pattern has significantly improved the biometric recognition field
due to its high level of stability and uniqueness. Such physical feature has
played an important role in security and other related areas. However,
presentation attacks, also known as spoofing techniques, can be used to bypass
the biometric system with artifacts such as printed images, artificial eyes,
and textured contact lenses. To improve the security of these systems, many
liveness detection methods have been proposed, and the first Internacional Iris
Liveness Detection competition was launched in 2013 to evaluate their
effectiveness. In this paper, we propose a hyperparameter tuning of the CASIA
algorithm, submitted by the Chinese Academy of Sciences to the third
competition of Iris Liveness Detection, in 2017. The modifications proposed
promoted an overall improvement, with an 8.48% Attack Presentation
Classification Error Rate (APCER) and 0.18% Bonafide Presentation
Classification Error Rate (BPCER) for the evaluation of the combined datasets.
Other threshold values were evaluated in an attempt to reduce the trade-off
between the APCER and the BPCER on the evaluated datasets and worked out
successfully.

Computer Vision and Pattern Recognition