Synthetic biometric data – such as fingerprints, face, iris and speech – can overcome some of the limitations associated with the use of real data in biometric systems. The focus of this work is on the iris biometric. Current methods for generating synthetic irides and ocular images have limitations in terms of quality, realism, intra-class diversity and uniqueness. Different methods are proposed in this thesis to overcome these issues while evaluating the utility of synthetic data for two biometric tasks: iris matching and presentation attack (PA) detection.Two types of synthetic iris images are generated: (1) partially synthetic and (2) fully synthetic. The goal of “partial synthesis” is to introduce controlled variations in real data. This can be particularly useful in scenarios where real data are limited, imbalanced, or lack specific variations. We present three different techniques to generate partially synthetic iris data: one that leverages the classical Relativistic Average Standard Generative Adversarial Network (RaSGAN), a novel Cyclic Image Translation Generative Adversarial Network (CIT-GAN) and a novel Multi-domain Image Translative Diffusion StyleGAN (MID-StyleGAN). While RaSGAN can generate realistic looking iris images, this method is not scalable to multiple domains (such as generating different types of PAs). To overcome this limitation, we propose CIT-GAN that generates iris images using multi-domain style transfer. To further address the issue of quality imbalance across different domains, we develop MID-StyleGAN that exploits the stable and superior generative power of diffusion based StyleGAN.The goal of “full synthesis” is to generate iris images with both inter and intra-class variations. In this regard, we propose two novel architectures, viz., iWarpGAN and IT-diffGAN. The proposed iWarpGAN focuses on generating iris images that are different from the identities in the training data using two transformation pathways: (1) Identity Transformation and (2) Style Transformation. On the other hand, Image Translative Diffusion-GAN (IT-diffGAN) projects input images onto the latent space of a diffusion GAN, identifying and manipulating the features most relevant to identity and style. By adjusting these features in the latent space, IT-diffGAN generates new identities while preserving image realism.A number of experiments are conducted using multiple iris and ocular datasets in order to evaluate the quality, realism, uniqueness, and utility of the synthetic images generated using the aforementioned techniques. An extensive analysis conveys the benefits and the limitations of each technique. In summary, this thesis advances the state of the art in iris and ocular synthesis by leveraging the prowess of GANs and Diffusion Models.
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