All objects radiate infrared energy invisible to the human eye, which can be imaged by infrared cameras, visualizing differences in temperature and/or emissivity of objects. Infrared imaging is an emerging technique for forensic investigators. The rapid, nondestructive, and noncontact features of infrared imaging indicate its suitability for many forensic applications, ranging from the estimation of time of death to the detection of blood stains on dark backgrounds. This paper provides an overview of the principles and instrumentation involved in infrared imaging. Difficulties concerning the image interpretation due to different radiation sources and different emissivity values within a scene are addressed. Finally, reported forensic applications are reviewed and supported by practical illustrations. When introduced in forensic casework, infrared imaging can help investigators to detect, to visualize, and to identify useful evidence nondestructively.

In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.

The early postmortem interval (PMI), i.e., the time shortly after death, can aid in the temporal reconstruction of a suspected crime and therefore provides crucial information in forensic investigations. Currently, this information is often derived from an empirical model (Henssge’s nomogram) describing posthumous body cooling under standard conditions. However, nonstandard conditions necessitate the use of subjective correction factors or preclude the use of Henssge’s nomogram altogether. To address this, we developed a powerful method for early PMI reconstruction using skin thermometry in conjunction with a comprehensive thermodynamic finite-difference model, which we validated using deceased human bodies. PMIs reconstructed using this approach, on average, deviated no more than ±38 minutes from their corresponding true PMIs (which ranged from 5 to 50 hours), significantly improving on the ±3 to ±7 hours uncertainty of the gold standard. Together, these aspects render this approach a widely applicable, i.e., forensically relevant, method for thermometric early PMI reconstruction.