Cranio-ID: Graph-Based Craniofacial Identification via Automatic Landmark Annotation in 2D Multi-View X-rays

2025-11-18

AI Summary

In forensic craniofacial identification, manual annotation of cranial landmarks is time-consuming and heavily reliant on expert knowledge, while existing automated methods suffer from insufficient large-scale validation and thus lack reliability. This paper proposes a graph-structured, multi-view 2D X-ray craniofacial identification framework. It is the first to integrate YOLO-Pose keypoint detection with cross-modal graph representation learning, enabling semantic alignment and cross-domain matching—between skull and face, and between sketch and face—via cross-attention mechanisms and optimal transport. Extensive experiments on the S2F and CUHK datasets demonstrate significant improvements in landmark localization accuracy and identity verification performance. The framework delivers a verifiable, robust, and fully automated solution for cross-modal forensic identification, advancing practical deployment in real-world forensic applications.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Automating craniometric landmark annotation in 2D X-rays to replace manual methods
• Improving reliability of cross-modal skull-to-face matching using graph representations
• Enhancing forensic identification accuracy through landmark-based graph correspondence

Innovation

• Automatic landmark annotation using YOLO-pose models
• Graph representation of craniofacial landmarks
• Cross-modal matching via attention and optimal transport