The Use of Scale‐Invariance Feature Transform Approach to Recognize and Retrieve Incomplete Shoeprints

Shoeprints left at the crime scene provide valuable information in criminal investigation due to the distinctive patterns in the sole. Those shoeprints are often incomplete and noisy. In this study, scale‐invariance feature transform is proposed and evaluated for recognition and retrieval of partial and noisy shoeprint images. The proposed method first constructs different scale spaces to detect local extrema in the underlying shoeprint images. Those local extrema are considered as useful key points in the image. Next, the features of those key points are extracted to represent their local patterns around key points. Then, the system computes the cross‐correlation between the query image and each shoeprint image in the database. Experimental results show that full‐size prints and prints from the toe area perform best among all shoeprints. Furthermore, this system also demonstrates its robustness against noise because there is a very slight difference in comparison between original shoeprints and noisy shoeprints.     

Shoeprint image retrieval and crime scene shoeprint image linking by using convolutional neural network and normalized cross correlation

A shoeprint image retrieval process aims to identify and match images of shoeprints found at crime scenes with shoeprint images from a known reference database. It is a challenging problem in the forensic discipline of footwear analysis because a shoeprint found at the crime scene is often imperfect. Recovered shoeprints may be partial, distorted, left on surfaces that do not mark easily, or perhaps come from shoes that do not transfer marks easily. In this study, we present a shoeprint retrieval method by using a convolutional neural network (CNN) and normalized cross-correlation (NCC). A pre-trained CNN was used to extract features from the pre-processed shoeprint images. We then employed NCC to compute a similarity score based on the extracted image features. We achieved a retrieval accuracy of 82% in our experiments, where a “successful” retrieval means that the ground truth image was returned in the top 1% of returned images. We also extend our shoeprint retrieval method to the problem of linking shoeprints recovered from crime scenes. This new method can provide a linkage between two crime scenes if the two recovered shoeprints originated from the same shoe. This new method achieved a retrieval accuracy of 88.99% in the top 20% of returned images.     

Determining Shoe Length from Partial Shoeprints

The length of a shoe has significant value in reflecting characteristics of the owner, and thereby, it can help in tracking suspects in criminal cases. However, the shoeprints left at a crime scene are often incomplete, resulting in difficulties in assessing shoe length. To find a way to estimate the shoe length from a partial shoeprint, 109 shoes with different sizes and general patterns were collected, and their prints were lifted using magnetic powder. Four feature points were defined on a shoeprint, and the longest distance between the feature points was defined as shoe length. Using linear, quadratic, and cubic regression analyses, a total of 15 equations were obtained between the shoe length and the other distances between the feature points. Out of these, the five most accurate equations were selected as the optimal equation. The verified test, including another 18 pairs of shoes, showed an average error of equations between 0.591 cm and 0.732 cm. The equations were also applied in two practical cases, resulting in good accuracy. The study demonstrates that shoe length could be determined from partial shoeprints through the proven equations.     

A novel technique for automatic shoeprint image retrieval

Recent developments in forensic science have resulted in large numbers of scene of crime images being collected for recording and analysis. Shoeprint images are no exception. In fact, these have recently been of great interest to police and forensic scientists as footwear evidence is now treated in the same manner as fingerprint and DNA evidence. Traditional approaches to shoeprint representations attempt to classify shoeprint images based on a number of possible patterns. Such approaches are difficult to implement in an automatic fashion without the intervention of a forensic specialist. This paper presents a robust algorithm for shoeprint matching based on Hu's moment invariants. It is shown that decreasing the resolution of images does not have a significant effect on the performance of the algorithm. It is also shown that the optimal performance of the proposed system is attained for images rotated by any angle.     

足迹信息的计算机管理与查询

目的　根据国内足迹计算机管理系统的现状和需求 ,研制了适合我国刑侦工作特点的足迹管理与查询系统。方法　通过建立鞋厂鞋样、现场足迹和犯罪嫌疑人足迹数据库 ,利用足迹分析与比对软件进行管理和查询。结果　该软件完全能实现足迹管理计算机化 ,降低技术人员的劳动强度 ,提高足迹信息的使用率。结论　该软件使用方便快捷 ,适合推广使用     

Developing a Spatial-Temporal Method for the Geographic Investigation of Shoeprint Evidence

Abstract:  This article examines the potential of a spatial-temporal method for analysis of forensic shoeprint data. The large volume of shoeprint evidence recovered at crime scenes results in varied success in matching a print to a known shoe type and subsequently linking sets of matched prints to suspected offenders. Unlike DNA and fingerprint data, a major challenge is to reduce the uncertainty in linking sets of matched shoeprints to a suspected serial offender. Shoeprint data for 2004 were imported from the Greater London Metropolitan Area Bigfoot database into a geographic information system, and a spatial-temporal algorithm developed for this project. The results show that by using distance and time constraints interactively, the number of candidate shoeprints that can implicate one or few suspects can be substantially reduced. It concludes that the use of space-time and other ancillary information within a geographic information system can be quite helpful for forensic investigation.     

Survey of 1276 shoeprint impressions and development of an automatic shoeprint pattern matching facility.

1276 shoeprints were collected at a scientific exhibition. Details regarding the age groups of the participants, style, size and manufacturer/brand of their shoes were recorded. The impressions were assigned to pattern groups showing that the most common pattern was present in only 1% of the population studied and most patterns were much less common. The impressions were digitized and a system developed for automatically sorting a database of images of outsole patterns in response to a reference image. The database images are ranked so that those from the same pattern group as the reference shoeprint are likely to be at the start of the list. A database of 486 complete shoeprint images belonging to 142 pattern groups was established with each group containing two or more examples. Tests of the system have shown that the first-ranked database image belongs to the same pattern group as the reference image 60% of the time and that a correct match appears within the first 5% of the ranked images 88% of the time. The system has translational and rotational invariance so that the spatial positioning of the reference shoeprint images does not have to correspond with the spatial positioning of the shoeprint images of the database. The performance of the system for matching partial shoeprints was also determined.     

Optimizing Ballistic Imaging Operations

Ballistic imaging systems can help solve crimes by comparing images of cartridge cases, which are recovered from a crime scene or test‐fired from a gun, to a database of images obtained from past crime scenes. Many U.S. municipalities lack the resources to process all of their cartridge cases. Using data from Stockton, CA, we analyze two problems: how to allocate limited capacity to maximize the number of cartridge cases that generate at least one hit, and how to prioritize the cartridge cases that are processed to maximize the usefulness (i.e., obtained before the corresponding criminal case is closed) of hits. The number of hits can be significantly increased by prioritizing crime scene evidence over test‐fires, and by ranking calibers by their hit probability and processing only the higher ranking calibers. We also estimate that last‐come first‐served increases the proportion of hits that are useful by only 0.05 relative to first‐come first‐served.     

Forensic Comparison of Soil Samples Using Nondestructive Elemental Analysis

Soil can play an important role in forensic cases in linking suspects or objects to a crime scene by comparing samples from the crime scene with samples derived from items. This study uses an adapted ED‐XRF analysis (sieving instead of grinding to prevent destruction of microfossils) to produce elemental composition data of 20 elements. Different data processing techniques and statistical distances were evaluated using data from 50 samples and the log‐LR cost (C_llr). The best performing combination, Canberra distance, relative data, and square root values, is used to construct a discriminative model. Examples of the spatial resolution of the method in crime scenes are shown for three locations, and sampling strategy is discussed. Twelve test cases were analyzed, and results showed that the method is applicable. The study shows how the combination of an analysis technique, a database, and a discriminative model can be used to compare multiple soil samples quickly.     

Hierarchical,Three‐Dimensional Measurement System for Crime Scene Scanning

We present a new generation of three‐dimensional (3D) measuring systems, developed for the process of crime scene documentation. This measuring system facilitates the preparation of more insightful, complete, and objective documentation for crime scenes. Our system reflects the actual requirements for hierarchical documentation, and it consists of three independent 3D scanners: a laser scanner for overall measurements, a situational structured light scanner for more minute measurements, and a detailed structured light scanner for the most detailed parts of tscene. Each scanner has its own spatial resolution, of 2.0, 0.3, and 0.05 mm, respectively. The results of interviews we have conducted with technicians indicate that our developed 3D measuring system has significant potential to become a useful tool for forensic technicians. To ensure the maximum compatibility of our measuring system with the standards that regulate the documentation process, we have also performed a metrological validation and designated the maximum permissible length measurement error E_MPE for each structured light scanner. In this study, we present additional results regarding documentation processes conducted during crime scene inspections and a training session.     

Use of footwear impressions in crime scene investigations assisted by computerised footwear collection system

Crime scene footwear classification systems can be utilized to look for possible suspects, to get models and brand names for crime scene impressions and to link crime scenes. In this work a computerised footwear classification system is presented. The classification coding of the system has been designed especially for partial footwear impressions which are the most typical in crime scenes. The system is flexible because it is possible to agree within a crime scene investigation unit the way in which the system is to be applied. If outsole patterns are classified accurately in processing for the database, time will be saved in the search process and vice versa. The accurate classification coding is propounded by the fact that only a few experienced users are needed to perform the classification and data storage and all police officers returning from crime scenes are able to perform searches very easily. The users only have to follow the defined rules for geometric shapes. The crime scene investigation unit of the Turku police tested the system for a period of 1.5 years. The system has proved to be of valuable assistance in investigation when several of the hints lead to early solving of a crime. Practical experiences are described.     

Using Spatial,Temporal and Evidence‐status Data to Improve Ballistic Imaging Performance

Firearms identification imaging systems help solve crimes by comparing newly acquired images of cartridge casings or bullets to a database of images obtained from past crime scenes. We formulate an optimization problem that bases its matching decisions not only on the similarity between pairs of images, but also on the time and spatial location of each new acquisition and each database entry. The objective is to maximize the detection probability subject to a constraint on the false positive rate. We use data on all cartridge casings matches detected in Israel during 2006–2008 to estimate most of the model parameters. We estimate matching accuracy from two different studies and predict that the optimal use of extraneous information would increase the detection probability from 0.931 to 0.987 and from 0.707 to 0.844, respectively. These improvements are achieved by favoring pairs of images that are closer together in space and time.     

The role and duties of the shoeprint/toolmark examiner in forensic laboratories

Shoeprint and toolmark examinations have been carried out for forensic purposes for a great number of years. According to literature, the first case of footwear identification dates back to the year 1786. This particular case was worked out by a local police constable. Who then, are the persons working in crime laboratories and specialising in shoeprint and toolmark examinations today? Are they responsible for some other kind of duties, and if ‘yes’, what kind of duties? To find answers to these questions, a questionnaire was prepared and sent to 78 forensic laboratories, 62 laboratories in 27 European countries and 16 laboratories in six countries outside Europe. Forty-three laboratories (55%) in 23 countries; 35 laboratories in 19 European countries and eight laboratories in four countries outside Europe returned the questionnaire. The conducted study shows that the shoeprint and toolmark examiners are performing a great variety of tasks besides shoeprint and toolmark examinations, and also that the tasks vary a lot from one laboratory to another.     

Upper‐Bounding the Incidence Rate of Associations Between Camouflage Uniforms and Surveillance Images*

Abstract: Camouflage garments can be associated with surveillance images of a crime scene even in the absence of unique wear marks or very high‐quality images. However, the probability of an accidental association, or incidence rate, is significant. The present work describes and validates a method for estimating the incidence rate based on a statistical model of the garment manufacturing process. The model was developed primarily for use with the current U.S. Army Combat Uniform (ACU), but can be applied to any camouflage garment. Eight garment manufacturers were studied, and all sources of variation in the manufacturing process were characterized. The marking and spreading procedures were found to be dominant and consistent sources of variation. However, some sources of variation, in particular those because of human operators, were not consistent enough to accurately characterize. Sources of variation that could not be well‐characterized were ignored in the statistical model, yielding a worst‐case estimate that is an upper‐bound to the true incidence rate. The model was evaluated for a variety of cases. Depending on the quality of the surveillance image, the manufacturing parameters, and the local population, incidence rates range from about 3% to negligibly small. The model was validated by returning to one manufacturer, and sampling a large number of completed garments and estimating empirical match probabilities. The empirical probabilities validated the estimates of the worst‐case incidence rate and also demonstrated that typical incidence rates are significantly lower.     

The use of simulated crime scenes in teaching undergraduate forensic sciences: Implementing an active learning approach to forensics

Incorporating a simulated crime scene into one’s pedagogy in forensic science undergraduate courses allows students the opportunity to experience a realistic scenario while demonstrating their knowledge and enhancing their critical thinking skills. The purpose of this paper is to examine an active learning approach to using simulated crime scenes to provide an impactful learning experience for students. While potentially challenging for the instructor, constructing a crime scene scenario can provide students with hands-on practical experience while helping to dispel forensic science misconceptions. Through many years of creating crime scenes, best practices for using such high impact activities in forensic science courses are described in detail, including preparatory exercises that culminate in the final crime scene scenario, considerations in preparing and constructing a crime scene activity, and supervising and assessing students once the activity begins. Three major challenges to implementing active learning exercises such as simulated crime scenes in undergraduate forensic science programs include high workload for instructors, limited supplies, and lack of suitable facilities. Workload solutions include instructors and departments considering the cost and benefit of course releases to improve curriculum and student enrollment. Supplies and facilities solutions involve innovation in reuse and repurposing of supplies, and instructor flexibility in using classroom and outdoor spaces.     

Self‐Strangulation Turning into Partial Hanging for a Suicide Victim

Hanging is the most common asphyxial method of suicide, whereas suicide by strangulation is unusual. Here, we are reporting a particular methodology of the asphyxial method of suicide in which a case of self‐strangulation culminated into partial hanging. A 30‐year‐old male wrapped one end of the cable wire around his neck. He then passed the other end over a curtain rod and tied that end around the right hand. He pulled the hand down, using the curtain rod as a fulcrum, to tighten the noose around the neck in an attempt to strangulate himself. However, he lost consciousness during the process and the body slipped down, pulling the right hand up which got stuck at the curtain rod. This led the body hanged in the kneeling position. This bizarre scenario raised suspicion of homicide but the crime scene, autopsy and victim characteristics were in favor of suicide.     

犯罪现场生物检材的发现、提取策略

在改进现场生物检材的发现和提取技术的同时,也要进行理论创新和体系构建,才能够更好地指导实践。全局的观点、具体案件具体分析和犯罪现场重建对于现场勘查中有效地发现、提取和利用生物检材是十分重要的。     

The Effect of Image Alterations on Identification Using Palmar Flexion Creases

Palmprints are identified using matching of minutia points, which can be time consuming for fingerprint experts and in database searches. This article analyzes the operational characteristics of a palmar flexion crease (PFC) identification software tool, using a dataset of 10 replicates of 100 palms, where the user can label and match palmar line features. Results show that 100 palmprint images modified 10 times each using rotation, translation, and additive noise, mimicking some of the characteristics found in crime scene palmar marks, can be identified with a 99.2% genuine acceptance rate and 0% false acceptance rate when labeled within 3.5 mm of the PFC. Partial palmprint images can also be identified using the same method to filter the dataset prior to traditional matching, while maintaining an effective genuine acceptance rate. The work shows that identification using PFCs can improve palmprint identification through integration with existing systems, and through dedicated palmprint identification applications.