The use of human identification has become an increasingly important factor over the past years, with
facial recognition being potentially the most common form used in daily life. But the face is not the
only biometric identifier that can be used as a feature for identification. In this work, we will investigate
chest X-rays as biometric identifiers. If they were proven to be viable, it would for example allow
identification post mortem, where common techniques currently have shortcomings . Also, a success
in such a way of identification may have far-reaching consequences and implications concerning data
protection and anonymity in the medical field.
In pattern recognition, the use of deep learning has proven to be successful in improving or even
replacing classical methods entirely. To test the limits of what is currently possible, a neural network
will be created that takes in two different x-ray scans as inputs and outputs a score measuring their
To increase the chances of success, a registration step will be incorporated in the preprocessing step. It
will be be implemented as a neural network layer, as this has proven to be effective in the past .
The thesis consists of the following milestones:
• Testing out the capabilities of different network architectures concerning the task of finding
matches in chest X-Ray scans
• Further enhancing the functionality by incorporating a layer into the network that is capable of
affine registrations, e. g. by means of a spatial transformer network 
The implementation should be done in Python.
 Ryudo Ishigami, Thi Thi Zin, Norihiro Shinkawa, and Ryuichi Nishii. Human identification using x-ray
image matching. In Proceedings of The International MultiConference of Engineers and Computer Scientists
2017, volume 1, pages 415–418, 2017.
 Grant Haskins, Uwe Kruger, and Pingkun Yan. Deep learning in medical image registration: a survey.
Machine Vision and Applications, 31(1–2), Jan 2020.
 Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu. Spatial transformer networks.
In Advances in Neural Information Processing Systems 28, pages 2017–2025. Curran Associates, Inc., 2015.