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

arXiv: https://arxiv.org/abs/2511.14411 · HTML: https://mlsanity.com/paper/2511.14411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Optimal Feature Transport for Cross-View Image Geo-Localization](https://mlsanity.com/paper/1907.05021/) (2019)
- [Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation](https://mlsanity.com/paper/2107.14724/) (2021)
- [Cross-Domain Image Matching with Deep Feature Maps](https://mlsanity.com/paper/1804.02367/) (2018)
- [Cross-Domain Identity Representation for Skull to Face Matching with Benchmark DataSet](https://mlsanity.com/paper/2507.08329/) (2025)
- [Self-Supervised Spatial Correspondence Across Modalities](https://mlsanity.com/paper/2506.03148/) (2025)
- [FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction](https://mlsanity.com/paper/2508.18031/) (2025)
- [GCF: Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2407.02361/) (2024)
- [Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition](https://mlsanity.com/paper/2507.14608/) (2025)

## Built on by (most cited first, 1 of 1)

- [Cranio-Diff: Diffusion-based Cross-domain Craniofacial Reconstruction with 2D X-ray Skull Guidance and Structural Identity Constraints](https://mlsanity.com/paper/2606.09699/) (2026)

## Abstract (arXiv)

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.