Segmentation-based ID preserving iris synthesis using generative adversarial networks

Kakani, Vijay; Jin, Cheng-Bin; Kim, Hakil

doi:10.1007/s11042-023-16508-1

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…This method uniquely allows for the variation in pupil size while ensuring the preservation of identity, addressing the challenge of accurately representing the nonlinear texture deformation of the iris. A similar objective was chosen in the work of Kakani et al [20]. In order to preserve the identity of the generated iris, the authors used three networks to segment portions of the eye (pupil, iris, sclera), extract individual features, and finally generate synthetic images.…”

Section: State Of the Art In Synthetic Iris Data Generationmentioning

confidence: 99%

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

Kordas,

Bartuzi-Trokielewicz,

Ołowski

et al. 2024

Sensors

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In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including—among others—images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no “identity leak” from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.

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Section: State Of the Art In Synthetic Iris Data Generationmentioning

confidence: 99%

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

Kordas,

Bartuzi-Trokielewicz,

Ołowski

et al. 2024

Sensors

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SynFAGnet: A Fully Automated Generative Network for Realistic Fire Image Generation

Nguyen,

Mai,

Nguyen

et al. 2024

Fire Technol

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This paper proposes a fully automated generative network (“SynFAGnet”) for automatically creating a realistic-looking synthetic fire image. SynFAGnet is used as a data augmentation technique to create diverse data for training models, thereby solving problems related to real data acquisition and data imbalances. SynFAGnet comprises two main parts: an object-scene placement net (OSPNet) and a local–global context-based generative adversarial network (LGC-GAN). The OSPNet identifies suitable positions and scales for fires corresponding to the background scene. The LGC-GAN enhances the realistic appearance of synthetic fire images created by a given fire object-background scene pair by assembling effects such as halos and reflections in the surrounding area in the background scene. A comparative analysis shows that SynFAGnet achieves better outcomes than previous studies for both the Fréchet inception distance and learned perceptual image patch similarity evaluation metrics (values of 17.232 and 0.077, respectively). In addition, SynFAGnet is verified as a practically applicable data augmentation technique for training datasets, as it improves the detection and instance segmentation performance.

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Forensic Iris Image Synthesis

Bhuiyan,

Czajka

2024

2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

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Segmentation-based ID preserving iris synthesis using generative adversarial networks

Cited by 3 publications

References 43 publications

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation

SynFAGnet: A Fully Automated Generative Network for Realistic Fire Image Generation

Forensic Iris Image Synthesis

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