부산대학교 도서관

Synthetic biometric samples are created with an ultimate goal of getting around privacy concerns, mitigating biases in biometric datasets, and reducing the sample acquisition effort to enable large-scale evaluations. The recent breakthrough in the development of neural generative models shifted the focus from image synthesis by mathematical modeling of biometric modalities to data-driven image generation. This paradigm shift on the one hand greatly improves the realism of synthetic biometric samples and therefore enables new use cases, but on the other hand new challenges and concerns arise. Despite their realism, synthetic samples have to be checked for appropriateness for the tasks they are intended which includes new quality metrics. Focusing on sample images of fingerprint, face, iris and vascular patterns, we highlight the benefits of using synthetic samples, review the use cases, and summarize and categorize the most prominent studies on synthetic biometrics aiming at showing recent progress and the direction of future research.