ABSTRACT
The 2009 National Academy of Sciences report (Strengthening Forensic Science in the United States: A Path Forward) and the 2016 PCAST report (Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods) made it clear that many traditional forensic disciplines lack of modern statistical approaches and quality control measures. This chapter surveys current procedures of evaluating shoeprint evidence and proposes a set of initial steps to improve the practice in this field. These include improving quality control (blind testing, performance testing), creating a large representative data base, developing a model that explains the creation of Randomly Acquired Characteristics (RACs) on shoe soles, reducing bias in the comparison process, studying various types of noise that exist on crime-scene prints. We also propose a tool called the Element Accidental Sensor that allows for semi-automated element and accidental recognition and digitization.
