Forensic toolmark comparisons are currently performed subjectively by humans, which leads to a lack of consistency and accuracy. There is little evidence that examiners can determine whether pairs of marks were made by the same tool or different tools. There is also little evidence that they can make this classification when marks are made under different conditions, such as different angles of attack or direction of mark generation. We generate original toolmark data in 3D, extract the signal from each toolmarks, and train an algorithm to compare toolmark signals objectively. We find that toolmark signals cluster by tool, and not by angle or direction. That is, the variability within tool, regardless of angle/direction, is smaller than the variability between tools. The known-match and known-non-match densities of the similarities of pairs of marks have a small overlap, even when accounting for dependencies in the data, making them a useful instrument for determining whether a new pair of marks was made by the same tool. We provide a likelihood ratio approach as a formal method for comparing toolmark signals with a measure of uncertainty. This empirically trained, open-source method can be used by forensic examiners to compare toolmarks objectively and thus improve the reliability of toolmark comparisons. This can, in turn, reduce miscarriages of justice in the criminal justice system.

We rely on experts all the time. If you need financial advice, you ask an expert. If you are sick, you visit a doctor, and as a juror you may listen to an expert witness. In the future, however, artificial intelligence (AI) might replace many of these people. In forensic science, the expert witness plays a vital role.

We rely on experts all the time. If you need financial advice, you ask an expert. If you are sick, you visit a doctor, and as a juror you may listen to an expert witness. In the future, however, artificial intelligence (AI) might replace many of these people. In forensic science, the expert witness plays a vital role.

For police, prosecuting attorneys, criminologists, and forensic scientists alike, emerging technologies will almost certainly revolutionize the future of forensic science, making the capture and conviction of criminals increasingly likely. These technologies can help investigators in missing persons cases, cold cases, sexual assault cases, and murder cases. Although potential dangers related to personal privacy have caused controversy about the use of these technologies, it seems clear that these ten cutting-edge innovations in the future of forensic science also promise enormous benefits to authorities, victims, victims' families, and society in general. In addition, people's faces change over time, and donning a pair of sunglasses or growing a beard can prevent the technology from making a match between the photos. Videos, which offer a series of images, should, in theory, provide a better chance for forensic science to identify a suspect, but that doesn't always happen, as the case of the Boston Marathon bombing proves: In a test of three facial recognition systems, only one identified Dzhokhar Tsarnaev, and none of them recognized Tamerlan Tsarnaev, who wore sunglasses.

This story was originally published by ProPublica. At the FBI Laboratory in Quantico, Virginia, a team of about a half-dozen technicians analyzes pictures down to their pixels, trying to determine if the faces, hands, clothes or cars of suspects match images collected by investigators from cameras at crime scenes. The unit specializes in visual evidence and facial identification, and its examiners can aid investigations by making images sharper, revealing key details in a crime or ruling out potential suspects. But the work of image examiners has never had a strong scientific foundation, and the FBI's endorsement of the unit's findings as trial evidence troubles many experts and raises anew questions about the role of the FBI Laboratory as a standard-setter in forensic science. FBI examiners have tied defendants to crime pictures in thousands of cases over the past half-century using unproven techniques, at times giving jurors baseless statistics to say the risk of error was vanishingly small. Much of the legal foundation for the unit's work is rooted in a 22-year-old comparison of bluejeans. Studies on several photo comparison techniques, conducted over the last decade by the FBI and outside scientists, have found they are not reliable. Since those studies were published, there's no indication that lab officials have checked past casework for errors or inaccurate testimony. Image examiners continue to use disputed methods in an array of cases to bolster prosecutions against people accused of robberies, murder, sex crimes and terrorism. The work of image examiners is a type of pattern analysis, a category of forensic science that has repeatedly led to misidentifications at the FBI and other crime laboratories. Before the discovery of DNA identification methods in the 1980s, most of the bureau's lab worked in pattern matching, which involves comparing features from items of evidence to the suspect's body and belongings. Examiners had long testified in court that they could determine what fingertip left a print, what gun fired a bullet, which scalp grew a hair "to the exclusion of all others." Research and exonerations by DNA analysis have repeatedly disproved these claims, and the U.S. Department of Justice no longer allows technicians and scientists from the FBI and other agencies to make such unequivocal statements, according to new testimony guidelines released last year. Though image examiners rely on similarly flawed methods, they have continued to testify to and defend their exactitude, according to a review of court records and examiners' written reports and published articles.

Since our inception, one of the mechanisms through which we have supported the community is by developing high-quality open source tools that lower barriers to working with scientific data. Equally important to our mission is to build capacity and promote researchers who are engaged in such practices within their disciplinary communities. This fellowship program is a unique opportunity for us to enable such individuals to have a bigger voice in their communities. This year, a diverse committee (comprised of Di Cook, Mine Cetinkaya-Rundel, Matt Jones, Ken Benoit and myself) reviewed 64 applications from researchers in various disciplines to select four winners. Emerging from this impressive pool of applicants are four outstanding researchers who constitute the 2018 Fellows.

If you've watched enough reruns of shows like CSI, Bones, and Law and Order, you probably know by now that when forensic specialists find DNA evidence, the suspect is often identified within the next couple of minutes--as soon as the team sticks the results of DNA analysis into a computer program. Although the real life process isn't quite as speedy, DNA certainly has been the highest bar for identification in forensics. But when it comes to hair samples of missing persons or those found at crime scenes, sequencing the proteins in those locks may work better than DNA. In a study published in the journal PLOS One on September 7, researchers at Lawrence Livermore National Laboratory in California demonstrated a method of extracting genetic information from proteins found in hair that is remarkably reliable. "Currently forensic science is very dependent on DNA," says primary author Glendon Parker, a biochemist at Livermore.