Robust Shoeprint Retrieval Method Based on Local‐to‐Global Feature Matching for Real Crime Scenes

In this study, an automatic and robust crime scene shoeprint retrieval method is proposed. As most shoeprints left at crime scenes are randomly partial and noisy, crime scene shoeprint retrieval is a challenging task. To handle partial, noisy shoeprint images, we employ denoising deep belief network (DBN) to extract local features and use spatial pyramid matching (SPM) to obtain a local‐to‐global matching score. In this study, 536 query shoeprint images from crime scenes and a large scale database containing 34,768 shoeprint images are used to evaluate the retrieval performance. Experimental results show that the proposed method outperforms other state‐of‐the‐art methods in terms of retrieval accuracy, feature dimension, and retrieval speed. The proposed method achieves a cumulative match score (CMS) of 65.67% at top 10 which is 5.60% higher than the second best performing method.     

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.     

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.     

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.     

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     

Lifting shoeprints using gelatin lifters and a hydraulic press

A method for lifting two-dimensional dust footwear marks on rough or porous surfaces, such as cardboard or cloth, using a hydraulic press, was examined. It was found that exerting pressure on the lifter by the press usually improves the quality of the results. When the shoeprints were on rough or soft surfaces, the prints transferred to the gelatin lifters were better than those obtained by the conventional method. In other cases, using the press did not improve the results but was much simpler to apply. Based on the results of this study, the hydraulic press/gelatin lifter method (the "press method") is used at the authors' laboratory, depending on the surface from which the shoeprint is to be lifted. It is the authors' intention to apply the method to other surfaces after finding the optimal pressure for surfaces with loose fibers.     

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.     

A comparative research of two lifting methods: electrostatic lifter and gelatin lifter

Two-dimensional dust shoeprints are often of very high resolution and contain unique features. Lifting these prints in the most effective method may contribute much to preserving these fine details. A research was conducted by experts from Israel and Switzerland to compare gelatin lifters and electrostatic lifters for lifting shoeprints. Several substrates were chosen, and on each material a set of dry dust shoeprints was made. A set of wet prints was made on paper as well. The shoeprints were approximately of the same quality, and the only variable was the nature of the material. On substrates indifferent to the method used, the preferable sequence was tested. Gelatin lifter was superior on most substrates and for wet prints. The superior sequence for using both methods is electrostatic lifting followed by gelatin lifter.     

Geographical variation of shoeprint comparison class correspondences

The underlying principles involved in the interpretation of shoeprint comparisons have become a topical subject due to criticisms in the 2009 National Academy of Science (NAS) report on forensic sciences[1]. Difficulties in the application and understanding of these principles were also highlighted in a recent court ruling [2–5] and subsequent discussion of the ruling. We report here a survey that may inform some aspects of this interpretation and discuss the implications of findings from this survey in the light of that court ruling and more importantly the NAS report.1,511 shoeprints were taken from student volunteers in Auckland, Wellington and Dunedin, New Zealand. 500 shoeprints were sampled from student volunteers at Australian universities. 100 from each of the University of Technology in Sydney, University of Queensland in Brisbane, University of Newcastle, Charles Sturt University in Bathurst and University of Canberra, Australia. These cities are distributed along the east coast of Australia.The shoeprints, taken from each country, were compared against each other for the presence of any pattern correspondences However shoeprints have not been compared between countries.In all locations the pattern of some common and many rare outsole patterns was repeated, with Converse Chuck Taylor All Stars and Vans Canvas Era common in all locations.     

The interpretation of shoeprint comparison class correspondences

The underlying principles involved in the interpretation of shoeprint comparisons have become a topical subject due to criticisms in the 2009 National Academy of Science (NAS) report on forensic sciences [1]. Difficulties in the application and understanding of these principles were also highlighted in a recent court ruling [2–5]. We report here a survey that may inform some aspects of this interpretation and discuss the implications of findings from this survey in the light of that court ruling and more importantly the NAS report.Five hundred shoeprints taken from student volunteers in Auckland, New Zealand were compared against each other for the presence of any pattern correspondences. Comparisons were undertaken of the full outsole and of smaller portions of the more common patterns.Of the 500 shoe impressions collected 488 (97.6%) were ultimately represented only once in the survey. The greatest number of corresponding patterns was for the most common brand of shoe (Converse Chuck Taylor All Star) and occurred in 3 of 500 observations. No instances of an imitation brand matching the authentic brand were found. Smaller sections of the common patterns showed a greater number of corresponding prints. However, the greatest number of matching partial patterns was again for the most common brand of shoe (Converse Chuck Taylor All Star) and occurred in 29 of 500 observations.We conclude that pattern match alone is of considerable evidential value even when the print is partial.     

Computer controlled scanner for laser enhanced photography

The use of a scanner to control the photography of fingerprints, shoeprints and other trace contacts which fluoresce under irradiation from an argon ion laser is described. This scanner has been built to cover areas varying in size from a single fingerprint to a shoeprint and provides for variable speed, step size and angle of radiation which are all very important in this highly specialised area of photography.     

Removing interfering contaminations from gelatin lifters

Gelatin lifter is widely used for recovering shoeprints from crime scenes. Dusty shoeprints removed from paper with loose fibers, cloth or plasterboard, might be concealed by the detached fibers. A novel technique to clean the gelatin lifter from the interfering contaminations, using adhesive lifters, was developed. The adhesive lifter is applied directly on the surface of the gelatin lifter, and is removed instantly. The adhesive lifter removes the upper layer of the attached material on the gelatin lifter. After removing the concealing material from the hidden imprint, the quality of the visualized imprint is improved. The cleaning process can be applied as many times as needed and the optimum number is different for each substrate. The small dust particles comprising the shoeprint remained attached to the sticky side of the gelatin lifter even though the adhesion force applied by the adhesive lifter is great. Repeating the procedure too many times could harm the quality of the print severely, yet the advantages of this method are greater than the risks of relocation of image quality reduction.     

Shoe wear patterns and pressure distribution under feet and shoes, determined by image analysis.

A method is outlined for extracting contours of the pressure exerted by feet on the ground, either directly or through the soles of shoes, and of the wear patterns on shoe soles. The method is applied to two types of image: shoeprint images made in the laboratory, and pedobarography images recording the distribution of pressure under the foot during a footstep. The first procedure in extracting the information from images involves low pass filtering in the Fourier domain, performed by a microcomputer and frame-grabbing system. The boundaries of the regions in the resultant images are then extracted from the filtered images to display the shape of constant pressure areas and of the general shoe-wear.     

Is re-creation of human identity possible using tooth prints? An experimental study to aid in identification

Enamel in teeth, as documented is the hardest substance in the entire human body. Tooth prints are the enamel rod end patterns on the tooth surface. Could this fact be a boon in utilizing it for an individual's identification in the hands of forensic experts? A study has been attempted towards the same. These tooth prints were recorded for 60 extracted tooth specimens after acid etching using a cellophane tape. Subsequently a digital image of the print was obtained which was subjected to biometric conversion using Verifinger standard SDK version 6.0 software followed by the use of Automated Fingerprint Identification System (AFIS) software for comparison of the tooth prints. In results we observed that tooth prints were composed of varied patterns and sub-patterns. A comparison was made between the tooth prints of different and the same individual and also between different classes of teeth (incisors, canines, premolars and molars). None of the patterns exhibited intra- and inter-individual similarity. Nor did any particular class of the tooth could be preferentially used over the other for an individual identification. This field demands a need for further exploration towards the use of tooth prints for establishing an individual's identity.     

Evaluation of Lip Prints on Different Supports Using a Batch Image Processing Algorithm and Image Superimposition

This study aimed to develop and to assess an algorithm to facilitate lip print visualization, and to digitally analyze lip prints on different supports, by superimposition. It also aimed to classify lip prints according to sex. A batch image processing algorithm was developed, which facilitated the identification and extraction of information about lip grooves. However, it performed better for lip print images with a uniform background. Paper and glass slab allowed more correct identifications than glass and the both sides of compact disks. There was no significant difference between the type of support and the amount of matching structures located in the middle area of the lower lip. There was no evidence of association between types of lip grooves and sex. Lip groove patterns of type III and type I were the most common for both sexes. The development of systems for lip print analysis is necessary, mainly concerning digital methods.     

Pistol image retrieval by shape representation

Databases have been used to record data in forensic science, such as fingerprints, shoeprints, and photos. In traditional databases, we use “text” as the “keyword” for retrieving data (text-keyword retrieval); however, in some applications, “text” is not proper to describe the searching target, and in this case, “image” plays an important role. In this paper, we use “image features” as the “keywords”, and show its potential for building up a prototype of pistol image databases.In current firearm databases of forensic science, the retrieval method is still by text-keyword retrieval. For experienced forensic examiners, this kind of databases may satisfy their requirement. However, for people who have little gun knowledge, how can they find the possible gun candidates or similar guns in the databases, if there are not any available words or marks on the gun? In this paper, we try to retrieve similar pistol images by the pistol shape instead of “text-keyword”. This method can narrow down the searching range while identifying pistols by firearm databases.There are more than 300 pistol images in our pistol image database. From the experimental results, we can retrieve the similar pistol images in top five candidates for each pistol image.