Towards Inclusive Biometrics: Synthetic Generation of Vitiligo Faces and Their Impact on Face Image Quality

André Dörsch, Andrea Laguna Liang, Christian Rathgeb, Christoph Busch; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2026, pp. 915-923

Recent studies indicate that Face Image Quality Assessment (FIQA) algorithms exhibit demographic biases, caused by physiological characteristics, particular skin pigmentation. In this work, we explore the potential impact of vitiligo, an autoimmune skin disease causing depigmentation patterns, on FIQA algorithms and associated biometric fairness. We introduce HDA-Synthetic Vitiligo Faces (SynVF), the first publicly available synthetic face dataset incorporating real vitiligo patterns of varying severity applied to synthetic identities. We utilize the Open Source Face Image Quality (OFIQ) framework and investigate the effect of vitiligo across pre-selected quality measures. We observe a concerning degradation of the Unified Quality Score (UQS) with increasing depigmentation, indicating that vitiligo tends to be misinterpreted as a quality defect rather than a depigmentation disease. By prompting additional synthetic vitiligo face images using OpenAI's 4o image generation model, we observed very low UQS scores on average, confirming that this bias trend generalizes beyond our proposed dataset. To mitigate the observed bias, we fine-tuned a Data-efficient Image Transformer (DeiT) for vitiligo classification and integrated its predicted probability into our proposed lightweight fairness-aware UQS adjustment that compensates for quality degradation. The classifier demonstrates strong generalization on real vitiligo faces, highlighting that synthetic data can be effectively used to support and improve the fairness of biometric systems.