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Footwear
Impression Analysis

Overarching GOALS

Developing new methods for assessing the strength of association between a crime scene print and a suspect’s shoe remains a priority for CSAFE researchers. Projects focus on gathering and analyzing information to create a score-based likelihood ratio framework for footwear examination. During the initial funding period, CSAFE teams at ISU, CMU and UCI advanced these efforts. Footwear research continues with CSAFE 2.0, led by researchers at ISU, UCI and WVU.

Hal Stern

Hal S. Stern

Provost and Executive Vice Chancellor and Chancellor's Professor, Co-Director of CSAFE

University of California, Irvine

CharlessFowlkes_web

Charless Fowlkes

Professor and Chancellor's Fellow

University of California, Irvine

kaplan-damary-naomi

Naomi Kaplan-Damary

Lecturer

Hebrew University of Jerusalem

SusanVanderplasColor

Susan Vanderplas

Assistant Professor

University of Nebraska, Lincoln

Alicia Carriquiry

Alicia Carriquiry

Distinguished Professor and President’s Chair, Director of CSAFE

Iowa State University

JacquelineSpeir1_web

Jacqueline Speir

Associate Professor

West Virginia University

Additional Team Members

Soyoung Park  sypark@iastate.edu

Richard Stone  rstone@iastate.edu

Steve Lund (NIST)  steven.lund@nist.gov

Martin Herman (NIST) martin.herman@nist.gov

focus Areas

It is believed that randomly acquired characteristics (RACs) are among the most discriminating features of shoeprints. Existing models to understand the spatial distribution of RACs rely on simplifying assumptions. One goal of our research is to develop and validate models that are more realistic for RACS. This project will also study the persistence of RACs over time (as the shoe is worn) and the repeatability and reproducibility of RAC detection by examiners.

Footwear comparison is conducted by experts in two broad stages. First, general properties such as the pattern, size and wear of the shoe sole are compared to the crime scene impression. If these do not fit, then the analysis is stopped and the pair is classified as a non-match. If the general properties fit, the forensic expert looks for randomly acquired characteristics (RACs) on the shoe sole and determines if they match RACs on the trace from the crime scene. Some CSAFE researchers are considering novel image-based approaches to footwear evidence. This project is focused on providing support for existing footwear analysis approaches through further study of RACs.

There is limited data available about the occurrence and persistence of RACs. This includes questions associated with whether RACs can be reliably annotated, whether RACs persist over time as the shoe is worn, and the relationship between RACs on a shoe and RACs found in crime scene impressions. Data from a recent study in Israel in which shoes were worn by individuals over the course of a year and repeated impressions were taken can be used to study these issues. Several individuals marked impressions made at different points in time by the same shoe. These data also include several instances of an individual marking the same impression at two points in time. The individuals in question were students trained by professional examiners. Though not themselves examiners we believe this unique data set is critical for developing approaches to analyzing the reproducibility (different examiner) and repeatability (same examiner) of RACs.

The goal of this project is to develop statistical and computational models to aid the interpretation of impression evidence left by shoe outsoles. We are developing statistical models that support robust reasoning about partial or obscured impression evidence and computational implementations of these models that can perform matching and retrieval for category-level (brand, size) identification from partial prints.

The goal of this project is to develop statistical and computational models to aid the interpretation of impression evidence left by shoe outsoles. Interpreting impression evidence requires reasoning about whether class-level and acquired characteristics of a candidate source are likely to have produced a given impression relative to other potential sources. We propose to develop statistical models that support robust reasoning about partial or obscured impression evidence and computational implementations of these models that can perform matching and retrieval for category-level (brand, size) identification from partial prints. Existing research in this area, including our own, has focused on matching test impressions with crime-scene evidence but has largely ignored forensic practice, which typically involves examination of the candidate source shoe itself and matching of physical features of the tread to evidence. To address this in a quantitative experimental framework, we propose to utilize high-resolution 3D scans of treads and develop methods that match features of these models to impression evidence and make predictions about the impressions a given tread might leave.

Footwear analysis is currently limited by the absence of reliable, publicly available databases that characterize the distribution of footwear characteristics and patterns. One approach to addressing this limitation is through the development of an instrument that can be deployed in public areas to passively collect images of outsoles of shoes. These data can enable estimation of the frequency of footwear patterns in a given region and thereby facilitate the transition to a likelihood ratio or Bayes Factor approach to the evaluation of footwear evidence. This is a novel project with the potential to have a large impact.

One of the biggest obstacles to development of quantitative and probabilistic methods for footwear impression evidence is that gathering data on the reference population or populations is incredibly difficult – the footwear used changes as new shoes are released, but also due to weather, geography, and other factors. This obstacle grows more complex when we consider the correct reference population: Should statistics be computed based on the general population, or based on a more difficult to measure population of “criminals”?

Project FWIII addresses these problems, leveraging a scanning device currently under development through an NIJ grant to acquire data from distinct geographic populations. We will develop relationships with local law enforcement in Iowa and Nebraska, eventually partnering with interested organizations to collect footwear data from populations which interact with law enforcement; during the same time period, we will collect data from public locations in the same region, facilitating comparison of the two reference populations. The collected data and associated metadata will be made available to the public in an online database which will serve as a resource for researchers and practitioners.

During the data collection period, we will manually annotate the collected images, identifying different class characteristics which may be of interest to examiners and researchers. These annotated images will be used to train more accurate feature recognition models, increasing the ability to automatically identify features in new images. Annotated images will also be made publicly available, serving as a resource for additional machine learning research in footwear class characteristics.

We will also work to characterize the main components of variability in class characteristic frequency from among options such as weather, time of day, weekday, population, and location. Leveraging this information, we will develop sampling guidelines to assist practitioners and other researchers who wish to collect data in their own geographic area.

The primary long-term goal of this project is to empower practitioners to collect footwear frequency data in their own jurisdictions, and to provide the hardware and software tools to support the use of the collected data in forensic applications. By providing open-access databases, we facilitate the development of additional tools for analyzing this type of data at the same time as we improve our own software and build up the body of literature relating to class characteristic frequency variation.

Researchers at ISU have developed and carried out limited testing of an algorithm called MC-COMP to quantify the similarity between two outsole impressions with the same class characteristics (Park and Carriquiry, 2020). The algorithm has a small error rate in identifying same/different source pairs (4%-5%); however, more testing on degraded images is needed. In addition, with our new partners at West Virginia University, we will use data from the application of this algorithm to identify features of shoeprint images that are associated with more difficult cases, with the goal of developing a quality metric for footwear images. This can enable automatic identification of image quality that can help interpret footwear evidence. 

Footwear impressions are commonly found in crime scenes, but until now footwear examiners have lacked the tools to objectively quantify the similarity between a crime scene impression to one or more test impressions. The task is challenging because crime scene impressions are often smudged or otherwise degraded. The ISU team has developed an algorithm called MC-COMP that produces a similarity score between two 2D outsole impressions that shows good performance (~96% accuracy) even when one of the images is degraded in a certain way. While promising, the algorithm has only been used on two shoe models and only on images degraded in the lab. 

Two-dimensional images consist of thousands of pixels, each with a certain gray intensity (in gray scale images).  MC-COMP uses a subset of interesting pixels selected using SURF (speeded-up robust features). To align two images, MC-COMP relies on the geometric arrangement of the selected pixels in local regions of the outsole that the examiner selects.  In this light, the algorithm we proposed is semi-automated and requires input by the examiner. We note that MC-COMP is a “whole outsole” method in that pixels of interest can correspond to the pattern of the outsole or to RACs (randomly acquired characteristics).

We propose to pursue the following research objectives:

  • Continue improving MC-COMP.  Now, the similarity score is produced by a random forest that combines three features.  We propose to explore the use of additional features and the possibility of stacking, by using the scores themselves as features in a different learning algorithm.
  • Greatly expand the type of outsoles on which we apply MC-COMP.  In particular, in collaboration with footwear examiners, NIST and the FBI, create a large database of realistic crime scene impressions with known ground truth to validate the algorithm and ensure that it will perform well in real casework. 
  • Explore the use of high-resolution instruments to collect both crime scene and test impressions. 
  • Assess the relationship between footwear impression quality and automated image comparison methods by developing a footwear image quality model based on independent image features/factors.  Resulting image quality predictors will be regressed against algorithm performance (match rank and ground truth) and expert predictions of quality in order to develop an independent quality model that can be used to inform weight of evidence estimates. 
  • In collaboration with practitioners, explore the steps needed to transition the new technologies to laboratories for use in real casework.

Knowledge Transfer

  • Type

Found 44 Results
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Computational Shoeprint Analysis for Forensic Science

Type: Research Area(s):

Published: 2024 | By: Samia Shafique

Shoeprints are a common type of evidence found at crime scenes and are regularly used in forensic investigations. However, their utility is limited by the lack of reference footwear databases that cover the large and growing number of distinct shoe…

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Challenges in Modeling, Interpreting, and Drawing Conclusions from Images as Forensic Evidence

Type: Research Area(s): ,,,

Published: 2024 | By: Karen Kafadar

When a crime is committed, law enforcement directs crime scene experts to obtain evidence that may be pertinent to identifying the perpetrator(s). Much of this evidence comes in the form of images, either digitally transcribed (e.g.,: fingerprints, handwriting), or as…

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Aligning Shoeprint Images that have nonlinear distortion effects

Type: Research Area(s): ,

Published: 2024 | By: Gautham Venkatasubramanian

Shoeprints are aligned before assessing similarity, and automatic alignment algorithms can handle differences in translation, rotation [1], and scale. But shoeprints recorded at a crime scene may be partials photographed at an angle without an L-scale, with perspective or other…

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Graph-Theoretic Techniques for Forensic Image Comparisons

Type: Research Area(s): ,

Published: 2024 | By: Gautham Venkatasubramanian

This presentation is from the 76th Annual Conference of the American Academy of Forensic Sciences (AAFS), Denver, Colorado, February 19-24, 2024.

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ShoeCase: A data set of mock crime scene footwear impressions

Type: Research Area(s):

Published: 2023 | By: Abigail Tibben

This project's main objective is to create an open-source database containing a sizeable number of high-quality images of shoe impressions. The Center for Statistics and Applications in Forensic Evidence (CSAFE) team collected images that represented those found at crime scenes…

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A finely tuned deep transfer learning algorithm to compare outsole images

Type: Research Area(s):

Published: 2023 | By: Moonsoo Jang

In forensic practice, evaluating shoeprint evidence is challenging because the differences between images of two different outsoles can be subtle. In this paper, we propose a deep transfer learning-based matching algorithm called the Shoe-MS algorithm that quantifies the similarity between…

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An automated alignment algorithm for identification of the source of footwear impressions with common class characteristics

Type: Research Area(s):

Published: 2024 | By: Hana Lee

We introduce an algorithmic approach designed to compare similar shoeprint images, with automated alignment. Our method employs the Iterative Closest Points (ICP) algorithm to attain optimal alignment, further enhancing precision through phase-only correlation. Utilizing diverse metrics to quantify similarity, we…

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A Semi-Automatic Tool for Footwear Impression Alignment

Type: Research Area(s): ,

Published: 2024 | By:

We introduce a semi-automatic alignment tool tailored for two similar footwear impressions. The term "semi-automatic" is used because the alignment process is primarily automated, yet users have the flexibility to fine-tune the results by adjusting certain parameters. This presentation provides…

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Shoeprint Alignment and Comparison using Maximum Cliques

Type: Research Area(s): ,

Published: 2023 | By: Gautham Venkatasubramanian

This presentation is from the 107th International Association for Identification (IAI) Annual Educational Conference, National Harbor, Maryland, August 20-26, 2023. Posted with permission of CSAFE.

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An algorithm for source identification of footwear impressions—its application on pristine shoeprints and crime-scene like shoeprints

Type: Research Area(s): ,

Published: 2023 | By: Hana Lee

This presentation is from the 107th International Association for Identification (IAI) Annual Educational Conference, National Harbor, Maryland, August 20-26, 2023. Posted with permission of CSAFE.

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CSAFE Project Update & ASCLD FRC Collaboration

Type: Research Area(s): ,,,,,

Published: 2022 | By: Jeff Salyards

This presentation highlighted CSAFE's collaboration with the ASCLD FRC Collaboration Hub.

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Source identification of shoeprints in mock crime scene using an algorithm based on automatic alignment

Type: Research Area(s): ,

Published: 2023 | By: Hana Lee

This presentation is from the 75th Anniversary Conference of the American Academy of Forensic Sciences, Orlando, Florida, February 13-18, 2023. Posted with permission of CSAFE

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Creating a Forensic Database of Shoeprints from Online Shoe-Tread Photos

Type: Research Area(s):

Published: 2023 | By: Samia Shafique

Shoe-tread impressions are one of the most common types of evidence left at crime scenes. However, the utility of such evidence is limited by the lack of databases of footwear prints that cover the large and growing number of distinct…

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A Comparison of Various Score-Based Likelihood Ratio (SLR) Methods for the Quantitative Assessment of Footwear Evidence

Type: Research Area(s): ,

Published: 2023 | By: Valerie Han

This presentation is from the 75th Anniversary Conference of the American Academy of Forensic Sciences, Orlando, Florida, February 13-18, 2023. Posted with permission of CSAFE.

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A New Algorithm for Source Identification of Look-alike Footwear Impressions Based on Automatic Alignment

Type: Research Area(s):

Published: 2022 | By: Hana Lee

Presentation at the International Association for Identification

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Center for Statistics and Application in Forensic Evidence Update

Type: Research Area(s): ,,

Published: 2022 | By: Alicia Carriquiry

The information below highlights a sample of current research initiatives led by the CSAFE team. Additional accomplishments in other forensic science disciplines will be discussed in subsequent issues of Forensic Science Review. Visit the CSAFE website www.forensicstats.org to learn more…

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Automatic Class Characteristic Recognition in Shoe Tread Images

Type: Research Area(s):

Published: 2022 | By: Jayden Stack

One of the fundamental problems in footwear forensics is that the distribution of class characteristics in the local population is not currently knowable. Surveillance devices for gathering this data are just half of the battle -- it is also necessary…

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Modeling And iNventory of Tread Impression System (MANTIS): The development, deployment and application of an active footwear data collection system

Type: Research Area(s): ,,

This CSAFE webinar was held on March 24, 2022. Presenters: Dr. Richard Stone Iowa State University Dr. Susan Vanderplas University of Nebraska, Lincoln Presentation Description: This webinar details the development, capabilities and successful deployment of the Modeling And iNventory of…


Evaluating the Reliability of Randomly Acquired Characteristics (RACs) Identification in Footwear Impression Evidence

Type: Research Area(s):

Published: 2021 | By: Corey Katz

Presented at American Association of Forensic Sciences (AAFS) 2021

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Footwear Research in CSAFE

Type: Research Area(s):

Published: 2021 | By: Alicia Carriquiry

This presentation provided an overview of CSAFE's footwear research and was presented at IAI in 2021

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