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On Performance Comparison of Real and Synthetic Iris Images

Jinyu Zuo, Natalia A. Schmid, Xiaohan Chen
2006 2006 International Conference on Image Processing  
We briefly describe a model based approach to synthesize iris images and focus on performance comparison for synthesized and real iris images.  ...  Comparison of synthetic and real data is performed at three levels of processing: (1) image level, (2) texture level, and (3) decision level.  ...  However, the results of extensive performance comparison of iris images from real and synthetic datasets presented in this paper have shown that real and synthetic iris data have similar performance in  ... 
doi:10.1109/icip.2006.313154 dblp:conf/icip/ZuoSC06 fatcat:yqixbzxfbfewdpppsouuv3evwq

A Model Based, Anatomy Based Method for Synthesizing Iris Images [chapter]

Jinyu Zuo, Natalia A. Schmid
2005 Lecture Notes in Computer Science  
In this work, we describe a model based/anatomy based method to synthesize iris images and evaluate the performance of synthetic irises by using a traditional Gabor filter based system and by comparing  ...  local independent components extracted from synthetic iris images with those from real iris images.  ...  We anticipate that synthetic data because of their excessive randomness and limited number of degrees of freedom compared to real iris images will provide overoptimistic bound on recognition performance  ... 
doi:10.1007/11608288_57 fatcat:4wrxiyywozbujkhnzxiabmzuzy

On Generation and Analysis of Synthetic Iris Images

Jinyu Zuo, Natalia A. Schmid, Xiaohan Chen
2007 IEEE Transactions on Information Forensics and Security  
A comprehensive comparison of synthetic and real data is performed at three levels of processing: (1) image level, (2) texture level, and (3) decision level.  ...  In this work, we describe a model based method to generate iris images and evaluate performance of synthetic irises by using a traditional Gabor filter based iris recognition system.  ...  We performed extensive performance comparison of iris images from real and synthetic data sets.  ... 
doi:10.1109/tifs.2006.890305 fatcat:ayvjlgki4ze7tkntpg3hycgy6u

A 3D Iris Scanner from a Single Image using Convolutional Neural Networks

Daniel P. Benalcazar, Jorge E. Zambrano, Diego Bastias, Claudio A. Perez, Kevin W. Bowyer
2020 IEEE Access  
A dataset of 26,520 real iris images from 120 subjects, and a dataset of 72,000 synthetic iris images with their aligned depthmaps were created.  ...  We analyzed the performance of our method to estimate the iris depth in multiple ways: using real step pyramid printed 3D models, comparing the results to those of a test set of synthetic images, comparing  ...  They would also like to thank the students at the School of Engineering, Universidad de Chile, who participated enthusiastically as volunteers for iris image acquisition.  ... 
doi:10.1109/access.2020.2996563 fatcat:eb7gfgcsjjbd3k26ixxape4djy

Iris Deidentification with High Visual Realism for Privacy Protection on Websites and Social Networks

Mauro Barni, Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri, Fabio Scotti
2021 IEEE Access  
FIGURE 9 . 9 Examples of synthetic iris textures computed using the proposed DCGAN. The images exhibit high visual realism and resemble RSMs computed from real irises.  ...  Specifically, we performed 3, 286 identity comparisons: one for each image in I-SOCIAL-DB. We performed this comparison using manually segmented masks.  ... 
doi:10.1109/access.2021.3114588 fatcat:2tdcxas53bf7pkk2irhxvh2ebm

Synthesis of large realistic iris databases using patch-based sampling

Zhuoshi Wei, Tieniu Tan, Zhenan Sun
2008 Pattern Recognition (ICPR), Proceedings of the International Conference on  
to real iris images in terms of visual appearance; (2) the proposed framework is able to generate databases with large capacity; (3) statistical performance shows that the synthetic iris images hold all  ...  the major characteristics of real iris images.  ...  We conducted a survey on three groups of people, who are asked to find real irises from a mixture image set (10 real vs. 10 synthetic).  ... 
doi:10.1109/icpr.2008.4761674 dblp:conf/icpr/WeiTS08 fatcat:eym4rym3uragbhoozigflyrwqe

Creating synthetic IrisCodes to feed biometrics experiments

Hugo Proenca, Joao C. Neves
2013 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications  
We experimentally confirmed that both the genuine and impostor distributions obtained on the artificial data closely resemble the values obtained in data sets of real irises.  ...  Even though there are methods to create images of artificial irises, there is no method exclusively focused in the synthesis of the iris biometric signatures (IrisCodes).  ...  This work also concerned about the bias that might be introduced by using synthetic data, having performed a comparison between the results observed for real and synthetic iris images.  ... 
doi:10.1109/bioms.2013.6656141 fatcat:rkxurvf6pnhh7mkjmjpizwrzla

Detecting medley of iris spoofing attacks using DESIST

Naman Kohli, Daksha Yadav, Mayank Vatsa, Richa Singh, Afzel Noore
2016 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)  
Examples of typical iris spoofing attacks are printed iris images, textured contact lenses, and synthetic creation of iris images.  ...  These algorithms usually perform very well on that particular attack. However, in real world applications, an attacker may perform different spoofing attacks.  ...  [1] proposed LU-CID descriptor and evaluated its efficacy on ATVS-FIr database of printed iris images. • Synthetic Iris Images: Venugopalan and Savvides [16] described a novel spoofing attack by creating  ... 
doi:10.1109/btas.2016.7791168 dblp:conf/btas/KohliYVSN16 fatcat:woechdwdjbedxfm7gevr6plx74

Recognition of motion blurred iris images

Jing Liu, Zhenan Sun, Tieniu Tan
2013 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)  
Experimental results on both synthetic and realworld motion blurred iris image databases demonstrate the effectiveness and efficiency of our methods.  ...  The texture details on iris patterns are lost in motion blurred images so it may cause recognition performance degradation.  ...  Table 1 . 1 Comparison of recognition performance on the synthetic and the real-world datasets.  ... 
doi:10.1109/btas.2013.6712691 dblp:conf/btas/LiuST13a fatcat:vjcxiq3q7nczlnwrl7ol65wefy

CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection [article]

Shivangi Yadav, Arun Ross
2020 arXiv   pre-print
The Styling Network helps the generator to drive the translation of images from a source domain to a reference domain and generate synthetic images with style characteristics of the reference domain.  ...  Evaluation using current state-of-the-art iris PAD methods demonstrates the efficacy of using such synthetically generated PA samples for training PAD methods.  ...  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the  ... 
arXiv:2012.02374v1 fatcat:gikkjozbira6fjidwd2xtm4xsm

Synthesis Of Iris Images Using Markov Random Fields

Sarvesh Makthal, Arun Ross
2005 Zenodo  
Publication in the conference proceedings of EUSIPCO, Antalya, Turkey, 2005  ...  In our clustering experiment, 97% of the real iris images and 100% of the synthetic iris images were classified into one group, and 75% of the Brodatz textures and 3% of the real iris images were classified  ...  The authors observe that the performance of competing algorithms on these synthetic databases, is comparable with their performance on real datasets, thereby suggesting the ability of SFINGE to realistically  ... 
doi:10.5281/zenodo.39238 fatcat:lasqsc7dfjgfzi2plbg4hnpp5m

Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms

Javier Galbally, Arun Ross, Marta Gomez-Barrero, Julian Fierrez, Javier Ortega-Garcia
2013 Computer Vision and Image Understanding  
image and the original one.  ...  The present work proposes a novel probabilistic approach based on genetic algorithms to reconstruct iris images from binary templates and analyzes the similarity between the reconstructed synthetic iris  ...  Javier Galbally would also like to thank Arun Ross of the Integrated Pattern Recognition and Biometrics Laboratory (iPRoBe) at West Virginia University for hosting him and collaborating with him during  ... 
doi:10.1016/j.cviu.2013.06.003 fatcat:mwwripbahneyxlwcygi2mck3oi

Iris Biometrics: Synthesis of Degraded Ocular Images

L. Cardoso, A. Barbosa, F. Silva, A. M. G. Pinheiro, H. Proenca
2013 IEEE Transactions on Information Forensics and Security  
Specifically, synthetic data are intended for use in the most important phases of those experiments: segmentation and signature encoding/matching.  ...  This paper describes a stochastic method for synthesizing ocular data to support experiments on iris recognition.  ...  Hence, it is important to observe how a state-of-the-art iris segmentation algorithm performs with synthetic data, allowing a comparison with segmentation performance on real data.  ... 
doi:10.1109/tifs.2013.2262942 fatcat:np4pxd7odvcrrou3uyzq2ip3qu

RIT-Eyes: Rendering of near-eye images for eye-tracking applications [article]

Nitinraj Nair, Rakshit Kothari, Aayush K. Chaudhary, Zhizhuo Yang, Gabriel J. Diaz, Jeff B. Pelz, Reynold J. Bailey
2020 arXiv   pre-print
We also report on the performance of two semantic segmentation architectures (SegNet and RITnet) trained on rendered images and tested on the original datasets.  ...  In this work, we introduce a synthetic eye image generation platform that improves upon previous work by adding features such as an active deformable iris, an aspherical cornea, retinal retro-reflection  ...  Errol Wood for providing the iris RGB textures from the Unity Eyes [34] and SynthEyes [35] . We thank Professor John Daugman [10] for providing infrared iris textures.  ... 
arXiv:2006.03642v1 fatcat:44txycquxbf6zp3aeflj6pc4s4

Mask-guided Style Transfer Network for Purifying Real Images [article]

Tongtong Zhao, Yuxiao Yan, Jinjia Peng, Huibing Wang, Xianping Fu
2019 arXiv   pre-print
However, due to the different distribution of synthetic images compared with real images, the desired performance cannot be achieved.  ...  Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions.  ...  In order to make a fairer comparison with our method, we reproduce SimGANs [1] , different from traditional methods which training on synthetic UnityEyes dataset and testing on real MPIIGaze dataset,  ... 
arXiv:1903.08152v1 fatcat:7wdcxembxvg4zdbralprqo5hzq
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