@inproceedings{
niu2024neural,
title={Neural Boneprint: Person Identification from Bones using Generative Contrastive Deep Learning},
author={Chaoqun Niu and Dongdong Chen and Ji-Zhe Zhou and Jian Wang and Xiang Luo and Quan-Hui Liu and YUAN LI and Jiancheng Lv},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=w24GYMUtN5}
}
Forensic person identification is of paramount importance in accidents and criminal investigations. Existing methods based on soft tissue or DNA can be unavailable if the body is badly decomposed, white-ossified, or charred. However, bones last a long time. This raises a natural question: ***can we learn to identify a person using bone data?***
We present a novel feature of bones called ***Neural Boneprint*** for personal identification. In particular, we exploit the thoracic skeletal data including chest radiographs (CXRs) and computed tomography (CT) images enhanced by the volume rendering technique (VRT) as an example to explore the availability of the neural boneprint. We then represent the neural boneprint as a joint latent embedding of VRT images and CXRs through a bidirectional cross-modality translation and contrastive learning. Preliminary experimental results on real skeletal data demonstrate the effectiveness of the Neural Boneprint for identification. We hope that this approach will provide a promising alternative for challenging forensic cases where conventional methods are limited. The code will be available at ***.
Neural Boneprint: Person Identification from Bones using Generative Contrastive Deep Learning
Chaoqun Niu Dongdong Chen Ji-Zhe Zhou Jian Wang Xiang Luo Quan-Hui Liu YUAN LI Jiancheng Lv