用进化算法自动优化颅面重叠，提升法医鉴定准确率
A Novel Evolutionary Method for Automated Skull-Face Overlay in Computer-Aided Craniofacial Superimposition
- 通过差分进化优化3D锥形软组织模型参数
- 在5个约束下实现颅面重叠，误差比现有方法降低12.3%
- 适合法医、刑侦人员用于尸体身份识别
颅面重叠是通过对比尸体颅骨与生前面部照片进行身份识别的法医技术。其中颅面重叠（SFO）步骤需将3D颅骨模型与2D面部图像对齐，通常依赖颅面解剖点对应。但软组织厚度个体差异大，导致重叠结果存在显著不确定性。本文提出Lilium方法，一种基于进化计算的自动化颅面重叠新策略。该方法采用3D锥形结构显式建模软组织变异，通过差分进化算法优化其参数。结合多个约束条件——解剖点匹配、相机参数一致性、头姿对齐、颅骨完全位于面部边界内、区域平行性——确保结果在解剖学、形态学和摄影上均合理。此模拟法医专家决策过程的方法，在准确性和鲁棒性上均优于当前最先进方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。