More Like this

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. 

   more » « less
2. 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))
3. 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. 

   more » « less
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. Iris Liveness Detection Competition (LivDet-Iris) – The 2023 Edition

   https://doi.org/10.1109/IJCB57857.2023.10448637  

   Tinsley, Patrick; Purnapatra, Sandip; Mitcheff, Mahsa; Boyd, Aidan; Crum, Colton; Bowyer, Kevin; Flynn, Patrick; Schuckers, Stephanie; Czajka, Adam; Fang, Meiling; et al (September 2023, IEEE)

   This paper describes the results of the 2023 edition of the “LivDet” series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark. Clarkson University and the University of Notre Dame contributed image datasets for the competition, composed of samples representing seven different PAI categories, as well as baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil Engineering and Architecture, and Hochschule Darmstadt contributed results for a total of eight PAD algorithms to the competition. Accuracy results are analyzed by different PAI types, and compared to human accuracy. Overall, the Fraunhofer IGD algorithm, using an attention-based pixel-wise binary supervision network, showed the best-weighted accuracy results (average classification error rate of 37.31%), while the Beijing University of Civil Engineering and Architecture’s algorithm won when equal weights for each PAI were given (average classification rate of 22.15%). These results suggest that iris PAD is still a challenging problem. 

   more » « less