Case Processing and Human Factors at Crime Laboratories

This CSAFE Center Wide Meeting Webinar was presented by Dr. Daniel Murrie and Dr. Sharon Kelley from University of Virginia on May 4, 2017.

Examining case processing to gauge the basic reliability of latent print examination is a crucial step in understanding and improving the statistical foundations for pattern evidence. It allows for better measurements of effective laboratory performance, the effects of altering case management procedures, and assessment of examiner-specific error rates. This presentation will focus on case processing of latent prints at the Houston Forensic Science Center.

A Generative Approach to Forensic Shoeprint Recognition

This CSAFE Center Wide Meeting Webinar was presented by Adam Kortylewski from the University of Basel in Switzerland on February 10, 2017.

The forensic analysis of impression evidence, such as fingerprints or shoeprints, plays a critical role in crime investigation. A common characteristic of impression evidence is that the pattern of interest is often latently hidden in structured clutter and severely occluded. This poses a major challenge to fully automated approaches for impression analysis. This talk discusses a generative approach that integrates the segmentation and classification of latent impressions in a common optimization framework.

Thinking About Likelihood Ratios for Pattern Evidence

This CSAFE Center Wide Meeting webinar was presented by Hal Stern from University of California on January 19, 2017.

The likelihood ratio has been proposed as a logical way to summarize forensic evidence. In pattern evidence disciplines; however, the application of likelihood ratios is challenging because of the high-dimensional data involved and the lack of relevant probability models (among other issues). Score-based likelihood ratios are one approach to handling the high-dimensional data issue. The webinar reviews likelihood ratios, their role in forensic science and the potential of score-based likelihood ratios.