Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies
It tackles data scarcity and privacy issues in biometrics, but is incremental as it reviews existing methods without introducing new ones.
The paper examines iris image synthesis methods to address the challenge of obtaining large, diverse biometric datasets, and discusses the risks of identity leakage from training data along with mitigation strategies.
This paper presents a comprehensive overview of iris image synthesis methods, which can alleviate the issues associated with gathering large, diverse datasets of biometric data from living individuals, which are considered pivotal for biometric methods development. These methods for synthesizing iris data range from traditional, hand crafted image processing-based techniques, through various iterations of GAN-based image generators, variational autoencoders (VAEs), as well as diffusion models. The potential and fidelity in iris image generation of each method is discussed and examples of inferred predictions are provided. Furthermore, the risks of individual biometric features leakage from the training sets are considered, together with possible strategies for preventing them, which have to be implemented should these generative methods be considered a valid replacement of real-world biometric datasets.