TRACE: Task-Defined Representation Alignment and Calibrated Exchange for Controllable Fingerprint Generation

Controllable fingerprint generation offers a promising way to expand limited biometric datasets and support presentation-attack research. Beyond visual realism, its practical utility depends on modifying a specified factor while preserving identity-related ridge patterns, non-target attributes, and fine-grained image details. This is challenging because fingerprint content, presentation material, and acquisition batch are often correlated and expressed through shared, spatially distributed visual patterns. We propose TRACE (Task-Defined Representation Alignment and Calibrated Exchange), a two-stage framework that separates factor-structured representation learning from generation-oriented refinement. In the first stage, Factor-Structured Information Extraction combines factor-wise supervised contrastive learning with image reconstruction to learn semantically aligned latent groups that retain the information needed for faithful generation. In the second stage, Structured Latent Distillation freezes the learned encoder and calibrates these groups through residual refinement and factor-specific spatial adaptation, enabling selective factor exchange while limiting unintended changes to non-target attributes. Experiments on a real-world fingerprint dataset and Shapes3D show that TRACE achieves state-of-the-art factor organization and controllable generation while maintaining competitive reconstruction quality. These results establish TRACE as an effective solution for task-defined controllable generation, combining accurate and interpretable factor control with faithful image synthesis.