TeLL-me what you cannot see: a vision-language framework for forensic mugshot augmentation

During criminal investigations, the availability of usable face imagery for persons of interest directly affects downstream investigative activities, including poster standardization and human-based search and review. In practice, agencies often face scarcity of high-quality images, heterogeneous capture conditions, and obsolescence, which can reduce the utility of available evidence and hinder timely information sharing. This paper introduces a forensic-oriented mugshot augmentation framework and evaluation protocol to support law-enforcement workflows with modern vision-language and generative models. The proposed modular pipeline optionally enhances low-quality inputs, extracts structured poster-style physical descriptors from a single image, and generates controlled synthetic portraits conditioned on those descriptors while monitoring identity consistency. By formalizing these steps and their associated measurements, the framework provides a reproducible reference for studying how such technologies behave under realistic constraints and for identifying failure cases relevant to forensic use. On the evaluated dataset, attribute extraction reached $$83.1%$$ 83.1 % (+ 2.3 percentage points over the original mugshots), and re-identification backends showed clear separation between same-subject and different-subject pairs (similarity ~ ∼ 0.70 vs. ~ ∼ 0.50). These results indicate that the framework can improve descriptor faithfulness and support identity-consistent augmentation under the tested conditions; limitations and forensic-relevant risks (e.g., appearance drift and demographic sensitivity) are explicitly discussed.