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1. LivDet iris 2017 — Iris liveness detection competition 2017

   https://doi.org/10.1109/BTAS.2017.8272763  

   Yambay, David ; Becker, Benedict ; Kohli, Naman ; Yadav, Daksha ; Czajka, Adam ; Bowyer, Kevin W. ; Schuckers, Stephanie ; Singh, Richa ; Vatsa, Mayank ; Noore, Afzel ; et al ( October 2017 , International Joint Conference on Biometrics (IJCB))

   Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015. This paper presents results of the third competition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submitted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36% and rate of accepted spoof samples of 14.71%. The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions. 

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2. Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition

   https://doi.org/10.1109/IJCB48548.2020.9304941  

   Das, Priyanka ; Mcfiratht, Joseph ; Fang, Zhaoyuan ; Boyd, Aidan ; Jang, Ganghee ; Mohammadi, Amir ; Purnapatra, Sandip ; Yambay, David ; Marcel, Sebastien ; Trokielewicz, Mateusz ; et al ( September 2020 , 2020 IEEE International Joint Conference on Biometrics (IJCB))

   null (Ed.)

   Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series: LivDet-Iris 2020. This year's competition introduced several novel elements: (a) incorporated new types of attacks (samples displayed on a screen, cadaver eyes and prosthetic eyes), (b) initiated LivDet-Iris as an on-going effort, with a testing protocol available now to everyone via the Biometrics Evaluation and Testing (BEAT)* open-source platform to facilitate reproducibility and benchmarking of new algorithms continuously, and (c) performance comparison of the submitted entries with three baseline methods (offered by the University of Notre Dame and Michigan State University), and three open-source iris PAD methods available in the public domain. The best performing entry to the competition reported a weighted average APCER of 59.10% and a BPCER of 0.46% over all five attack types. This paper serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments. 

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3. LivDet-Iris 2015 - Iris Liveness Detection Competition 2015

   https://doi.org/10.1109/isba.2017.7947701  

   Yambay, David ; Walczak, Brian ; Schuckers, Stephanie ; Czajka, Adam ( February 2017 , 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA))
4. Improving Off-angle Iris Recognition using Iris Quadrant Masking

   https://doi.org/10.1109/SoutheastCon48659.2022.9764073  

   Kota, Jitendra Sai ; Karakaya, Mahmut ( March 2022 , IEEE SoutheastCon 2022)
5. End-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation

   Jalilian, E ; Karakaya, M ; Uhl, A. ( January 2020 , 2020 International Conference of the Biometrics Special Interest Group (BIOSIG))

   null (Ed.)

   While deep learning techniques are increasingly becoming a tool of choice for iris segmentation, yet there is no comprehensive recognition framework dedicated for off-angle iris recognition using such modules. In this work, we investigate the effect of different gaze-angles on the CNN based off-angle iris segmentations, and their recognition performance, introducing an improvement scheme to compensate for some segmentation degradations caused by the off-angle distortions. Also, we propose an off-angle parameterization algorithm to re-project the off-angle images back to frontal view. Taking benefit of these, we further investigate if: (i) improving the segmentation outputs and/or correcting the iris images before or after the segmentation, can compensate for off-angle distortions, or (ii) the generalization capability of the network can be improved, by training it on iris images of different gaze-angles. In each experimental step, segmentation accuracy and the recognition performance are evaluated, and the results are analyzed and compared. 

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