From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation.

Techniques from artificial intelligence (AI) can be used in forensic evidence evaluation and are currently applied in biometric fields. However, it is generally not possible to fully understand how and why these algorithms reach their conclusions. Whether and how we should include such 'black box' algorithms in this crucial part of the criminal law system is an open question that has not only scientific but also ethical, legal, and philosophical angles.

Improving the evidential value of low-quality face images with aggregation of deep neural network embeddings.

In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal proceedings. On the other hand, in forensic casework, multiple images of the person of interest are usually available.

Diverse types of expertise in facial recognition.

Facial recognition errors can jeopardize national security, criminal justice, public safety and civil rights. Here, we compare the most accurate humans and facial recognition technology in a detailed lab-based evaluation and international proficiency test for forensic scientists involving 27 forensic departments from 14 countries. We find striking cognitive and perceptual diversity between naturally skilled super-recognizers, trained forensic examiners and deep neural networks, despite them achieving equivalent accuracy.

From facial images of different quality to score based LR.

A simple method is proposed to assess the quality of a trace facial image in the context of the facial recognition system used using the similarity scores with low quality different-source facial images, defined as the Confusion Score (CS). Methods are proposed to calculate the probability of finding the correct facial image in a database using low quality images for investigational purposes using the CS, as well as calculation of the Likelihood Ratio (LR) for comparison of low quality trace facial images with good quality reference facial images, based on the assessed CS of the trace image. Improvement of performance of an LR-system using training datasets stratified on CS over the use of pooled data is demonstrated.

Interpol review of imaging and video 2016-2019.

This review paper covers the forensic-relevant literature in imaging and video analysis from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.