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Automatic Acquisition and Identification of Footwear Class Characteristics

Award Information

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Description of original award (Fiscal Year 2019, $380,405)

Most footwear evidence in the United States is evaluated on the basis of class characteristics, without regard for individualizing information. Unfortunately, it is very difficult to assess the strength of a class characteristic match due to lack of data about the frequency of class characteristics within a population. This project addresses the lack of data through the development of equipment and software which can automate the data collection process. The system we propose to develop during Phase 1 of this project will be able to record images of shoe soles and uppers from pedestrians, and automatically identify class characteristics such as brand, regions of shoe tread, and geometric shapes. The shoe scanner will be capable of data collection outdoors over a 24-72 hour period without human intervention, and the automatic processing software will translate the collected images into data which can be used to by examiners to provide supporting information during testimony about the frequency of specific geometric shapes, shoe brands, and other relevant features. To demonstrate the potential of the system, during Phase 2 we will use the scanner and software to collect data from a set of locations in Ames, Iowa over the course of a year. We will use the collected data to assess variation in class characteristics as a result of time and weather conditions, and will provide an interactive application for practitioners to explore the collected data. In addition, the full database of collected images will be made available to the public, providing a valuable reference database for development of new methods for computational footwear analysis and statistical modeling of footwear characteristics. This work will provide the foundation for several undergraduate research projects, one masters project, and at least one PhD dissertation in statistics, and is expected to generate publications in optics engineering, statistics, computational image analysis, and forensics journals. All design documents, computer software, and data assembled as a result of this project will be made available to the public. The development of automatic methods for collecting footwear images from the population will provide practitioners with the ability to leverage statistics to quantify the significance of a class characteristic match. Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14). CA/NCF
Date Created: September 13, 2019