Craniofacial Superimposition is a forensic technique for identifying skeletal remains by comparing a post-mortem skull with ante-mortem facial photographs. A critical step in this process is Skull-Face Overlay (SFO). This stage involves aligning a 3D skull model with a 2D facial image, typically guided by cranial and facial landmarks' correspondence. However, its accuracy is undermined by individual variability in soft-tissue thickness, introducing significant uncertainty into the overlay. This paper introduces Lilium, an automated evolutionary method to enhance the accuracy and robustness of SFO. Lilium explicitly models soft-tissue variability using a 3D cone-based representation whose parameters are optimized via a Differential Evolution algorithm. The method enforces anatomical, morphological, and photographic plausibility through a combination of constraints: landmark matching, camera parameter consistency, head pose alignment, skull containment within facial boundaries, and region parallelism. This emulation of the usual forensic practitioners' approach leads Lilium to outperform the state-of-the-art method in terms of both accuracy and robustness.

翻译：颅面迭合是一种通过比较死后颅骨与生前面部照片来识别骨骼遗骸的法医技术。该过程中的关键步骤是颅骨-面部覆盖。此阶段涉及将三维颅骨模型与二维面部图像对齐，通常依据颅骨和面部标志点的对应关系。然而，其准确性因个体软组织厚度差异而受到削弱，给覆盖过程带来了显著的不确定性。本文提出Lilium，一种自动化进化方法，旨在提高颅骨-面部覆盖的准确性和鲁棒性。Lilium采用基于三维锥体的表示显式建模软组织变异，其参数通过差分进化算法进行优化。该方法通过组合约束条件——标志点匹配、相机参数一致性、头部姿态对齐、颅骨位于面部边界内以及区域平行性——强制执行解剖学、形态学和摄影学上的合理性。这种对法医从业者常规方法的模拟使Lilium在准确性和鲁棒性方面均优于当前最先进的方法。