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Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation.
Adrian Kordas1, Ewelina Bartuzi-Trokielewicz1, Michał Ołowski1
1Department of Biometrics, NASK-National Research Institute, 01-045 Warsaw, Poland.
Sensors (Basel, Switzerland)
|April 13, 2024
Summary
Generative adversarial networks (GANs) can create realistic synthetic iris images for biometric systems. These generated irises show sufficient texture for differentiation, enhancing training datasets without identity leaks.
Area of Science:
- Biometrics
- Computer Vision
- Artificial Intelligence
Background:
- Generative techniques, especially Generative Adversarial Networks (GANs), enable synthetic biometric data creation.
- Iris recognition systems benefit from diverse and large datasets for improved accuracy.
Purpose of the Study:
- To generate synthetic human iris images using the StyleGAN3 model.
- To analyze synthetic iris texture density and suitability for feature extraction.
- To evaluate generated irises in both Cartesian and polar coordinate representations.
Main Methods:
- Utilized StyleGAN3 for synthetic iris image generation.
- Generated 1327 unique synthetic iris samples.
- Tested generated irises using OSIRIS and wordlcoin-openiris software for differentiation and identity leak detection.
Main Results:
- No identity leak from the training set was observed in generated irises.
- Generated irises possess sufficient unique textural information for successful differentiation.
- Synthetic irises were successfully differentiated from each other and from real iris samples.
Conclusions:
- Synthetic iris data generation shows significant potential for augmenting training datasets.
- Generated iris data can enhance the performance, accuracy, and robustness of iris recognition systems.
- Exploring both Cartesian and polar representations aids understanding of synthetic data benefits for biometrics.
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