In forensic craniofacial identification and in many biomedical applications, craniometric landmarks are important. Traditional methods for locating landmarks are time-consuming and require specialized knowledge and expertise. Current methods utilize superimposition and deep learning-based methods that employ automatic annotation of landmarks. However, these methods are not reliable due to insufficient large-scale validation studies. In this paper, we proposed a novel framework Cranio-ID: First, an automatic annotation of landmarks on 2D skulls (which are X-ray scans of faces) with their respective optical images using our trained YOLO-pose models. Second, cross-modal matching by formulating these landmarks into graph representations and then finding semantic correspondence between graphs of these two modalities using cross-attention and optimal transport framework. Our proposed framework is validated on the S2F and CUHK datasets (CUHK dataset resembles with S2F dataset). Extensive experiments have been conducted to evaluate the performance of our proposed framework, which demonstrates significant improvements in both reliability and accuracy, as well as its effectiveness in cross-domain skull-to-face and sketch-to-face matching in forensic science.

翻译：在法医颅面识别及众多生物医学应用中，颅骨测量特征点具有重要作用。传统的特征点定位方法耗时且需要专业知识与技能。现有方法多采用叠加配准或基于深度学习的方法实现特征点的自动标注，但由于缺乏大规模验证研究，这些方法的可靠性不足。本文提出了一种新颖的框架Cranio-ID：首先，利用我们训练的YOLO-pose模型，在二维颅骨图像（即面部X射线扫描）及其对应的光学图像上实现特征点的自动标注；其次，通过将特征点构建为图表示，并利用交叉注意力与最优传输框架，实现两种模态图之间的语义对应匹配。我们提出的框架在S2F和CUHK数据集（CUHK数据集与S2F数据集相似）上进行了验证。通过大量实验评估了所提框架的性能，结果表明其在可靠性与准确性上均有显著提升，同时在法医学中的跨域颅骨-人脸匹配及素描-人脸匹配任务中展现出有效性。