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：颅面叠加是一种通过比对死后颅骨与生前面部照片来识别骨骼遗骸的法医学技术。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，通常依据颅骨与面部标志点的对应关系进行。然而，由于个体软组织厚度的差异性，该步骤的准确性受到严重影响，为覆盖过程引入了显著的不确定性。本文提出Lilium方法，这是一种用于提升SFO准确性与鲁棒性的自动化进化方法。Lilium采用基于三维锥体的表征方式对软组织变异性进行显式建模，其参数通过差分进化算法进行优化。该方法通过组合多类约束来保障解剖学、形态学及摄影学合理性：标志点匹配、相机参数一致性、头部姿态对齐、颅骨包含于面部边界内以及区域平行性。这种对法医学从业者常规操作流程的模拟，使得Lilium在准确性与鲁棒性方面均超越了现有最优方法。