Pattern matching of footwear Impressions

One of the most frequently secured types of evidence at crime scenes are footware impressions. Identifying the brand and model of the footware can be crucial to narrowing the search for suspects. This is done by forensic experts by comparing the evidence found at the crime scene with a huge list of reference impressions. In order to support the forensic experts an automatic retrieval of the most likely matches is desired.In this thesis different techniques are evaluated to recognize and match footwear impressions, using reference and real crime scene shoeprint images. Due to the conditions in which the shoeprints are found (partial occlusions, variation in shape) a translation, rotation and scale invariant system is needed. A VLAD (Vector of Locally Aggregated Descriptors) encoder is used to clustering descriptors obtained using different approaches, such as SIFT (Scale-Invariant Feature Transform), Dense SIFT in a Triplet CNN (Convolutional Neural Network). These last two approaches provide the best performance results when the parameters are correctly adjusted, using the Cumulative Matching Characteristic curve to evaluate it.En esta tesis se evalúan diferentes técnicas para reconocer y emparejar impresiones de calzado, utilizando imágenes de referencia y de escenas reales de crimen. Debido a las condiciones en que se encuentran las impresiones (oclusiones parciales, variaciones de forma) se necesita un sistema invariante ante translación, rotación y escalado. Para ello se utiliza un codificador VLAD (Vector of Locally Aggregated Descriptors) para agrupar descriptores obtenidos en diferentes enfoques, como SIFT (Scale-Invariant Feature Transform), Dense SIFT y Triplet CNN (Convolutional Neural Network). Estos dos últimos enfoques proporcionan los mejores resultados una vez los parámetros se han ajustado correctamente, utilizando la curva CMC (Characteristic Matching Curve) para realizar la evaluación.En aquesta tesi s'avaluen diferents tècniques per reconèixer i aparellar impressions de calçat, utilitzant imatges de referència i d'escenes reals de crim. Degut a les condicions en què es troben les impressions (oclusions parcials, variació de forma ) es necessita un sistema invariant davant translació, rotació i escalat. Per això s'utilitza un codificador VLAD (Vector of Locally Aggregated Descriptors) per agrupar descriptors obtinguts en diferents enfocaments, com SIFT (Scale-Invariant Feature Transform), Dense SIFT i Triplet CNN (Convolutional Neural Network). Aquests dos últims enfocaments proporcionen els millors resultats un cop els paràmetres s'han ajustat correctament, utilitzant la corba CMC (Characteristic Matching Curve) per realitzar l'avaluació

Quantitative assessment of the discrimination potential of class and randomly acquired characteristics for crime scene quality shoeprints

Footwear evidence has tremendous forensic value; it can focus a criminal investigation, link suspects to scenes, help reconstruct a series of events, or otherwise provide information vital to the successful resolution of a case. When considering the specific utility of a linkage, the strength of the connection between the source footwear and an impression left at the scene of a crime varies with the known rarity of the shoeprint itself, which is a function of the class characteristics, as well as the complexity, clarity, and quality of randomly acquired characteristics (RACs) available for analysis. To help elucidate the discrimination potential of footwear as a source of forensic evidence, the aim of this research was three-fold.;The first (and most time consuming obstacle) of this study was data acquisition. In order to efficiently process footwear exemplar inputs and extract meaningful data, including information about randomly acquired characteristics, a semi-automated image processing chain was developed. To date, 1,000 shoes have been fully processed, yielding a total of 57,426 RACs characterized in terms of position (theta, r, rnorm), shape (circle, line/curve, triangle, irregular) and complex perimeter (e.g., Fourier descriptor). A plot of each feature versus position allowed for the creation of a heat map detailing coincidental RAC co-occurrence in position and shape. Results indicate that random chance association is as high as 1:756 for lines/curves and as low as 1:9,571 for triangular-shaped features. However, when a detailed analysis of the RAC\u27s geometry is evaluated, each feature is distinguishable.;The second goal of this project was to ascertain the baseline performance of an automated footwear classification algorithm. A brief literature review reveals more than a dozen different approaches to automated shoeprint classification over the last decade. Unfortunately, despite the multitude of options and reports on algorithm inter-comparisons, few studies have assessed accuracy for crime-scene-like prints. To remedy this deficit, this research quantitatively assessed the baseline performance of a single metric, known as Phase Only Correlation (POC), on both high quality and crime-scene-like prints. The objective was to determine the baseline performance for high quality exemplars with high signal-to-noise ratios, and then determine the degree to which this performance declined as a function of variations in mixed media (blood and dust), transfer mechanisms (gel lifters), enhancement techniques (digital and chemical) and substrates (ceramic tiles, vinyl tiles, and paper). The results indicate probabilities greater than 0.850 (and as high as 0.989) that known matches will exhibit stochastic dominance, and probabilities of 0.99 with high quality exemplars (Handiprints or outsole edge images).;The third and final aim of this research was to mathematically evaluate the frequency and similarity of RACs in high quality exemplars versus crime-scene-like impressions as a function of RAC shape, perimeter, and area. This was accomplished using wet-residue impressions (created in the laboratory, but generated in a manner intended to replicate crime-scene-like prints). These impressions were processed in the same manner as their high quality exemplar mates, allowing for the determination of RAC loss and correlation of the entire RAC map between crime scene and high quality images. Results show that the unpredictable nature of crime scene print deposition causes RAC loss that varies from 33-100% with an average loss of 85%, and that up to 10% of the crime scene impressions fully lacked any identifiable RACs. Despite the loss of features present in the crime-scene-like impressions, there was a 0.74 probability that the actual shoe\u27s high quality RAC map would rank higher in an ordered list than a known non-match map when queried with the crime-scene-like print. Moreover, this was true despite the fact that 64% of the crime-scene-like impressions exhibit 10 or fewer RACs

Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions

We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr

The use of low cost virtual reality and digital technology to aid forensic scene interpretation and recording

© Cranfield University 2005. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner.Crime scenes are often short lived and the opportunities must not be lost in acquiring sufficient information before the scene is disturbed. With the growth in information technology (IT) in many other scientific fields, there are also substantial opportunities for IT in the area of forensic science. The thesis sought to explore means by which IT can assist and benefit the ways that forensic information can be illustrated and elucidated in a logical manner. The central research hypothesis considers that through the utilisation of low cost IT, the visual presentation of information will be of significant benefit to forensic science in particular for the recoding of crime scenes and its presentation in court. The research hypothesis was addressed by first exploring the current crime scene documentation techniques; their strengths and weaknesses, giving indication to the possible niche that technology could occupy within forensic science. The underlying principles of panoramic technology were examined, highlighting its ability to express spatial information efficiently. Through literature review and case studies, the current status of the technology within the forensic community and courtrooms was also explored to gauge its possible acceptance as a forensic tool. This led to the construction of a low cost semi-automated imaging system capable of capturing the necessary images for the formation of a panorama. This provides the ability to pan around; effectively placing the viewer at the crime scene. Evaluation and analysis involving forensic personnel was performed to assess the capabilities and effectiveness of the imaging system as a forensic tool. The imaging system was found to enhance the repertoire of techniques available for crime scene documentation; possessing sufficient capabilities and benefits to warrant its use within the area of forensics, thereby supporting the central hypothesis

Watch your step!: a frustrated total internal reflection approach to forensic footwear imaging

Forensic image retrieval and processing are vital tools in the fight against crime e.g. during fingerprint capture. However, despite recent advances in machine vision technology and image processing techniques (and contrary to the claims of popular fiction) forensic image retrieval is still widely being performed using outdated practices involving inkpads and paper. Ongoing changes in government policy, increasing crime rates and the reduction of forensic service budgets increasingly require that evidence be gathered and processed more rapidly and efficiently. A consequence of this is that new, low-cost imaging technologies are required to simultaneously increase the quality and throughput of the processing of evidence. This is particularly true in the burgeoning field of forensic footwear analysis, where images of shoe prints are being used to link individuals to crime scenes. Here we describe one such approach based upon frustrated total internal reflection imaging that can be used to acquire images of regions where shoes contact rigid surfaces

Forensic Investigation of Static Bare footprints Sampled from Three Distinct Races; White British, Chinese And Indians.

Bare footprints, marks or impressions found at crime scenes can potentially provide criminal investigators with intelligence relating to the stature, gait of a perpetrator or aid the reconstruction of a crime scene. Currently, little is known about the inter- and intra-variations in bare footprint morphologies or the prevalence of certain characteristics in bare footprints from distinct races. To understand such variability requires large datasets of bare footprints. One of the primary aims of this thesis was to develop a novel, inexpensive method to record control samples and use the method to generate large datasets of bare footprints. The reliability of this method was investigated, and the qualitative and quantitative results indicated that there was repeatability and comparability between the new method (lotion) and the industry standard existing methods, for example, the inkless shoeprint kit and fingerprint ink. Following the successful testing of the lotion method, the lotion was used to gather static control bare footprints from three distinct races, White British (n = 25); Chinese (n =25); and Indian (n = 25). The quantitative data consisting of the footprint dimensions were converted to ratios. In addition, the foot outline was converted to morphological landmarks and the data was analysed using principle component analysis (PCA) and model-based cluster analysis (MBCA) to investigate the relationships between the three races. The results showed that the data from the three races could be placed into their respective racial groups using the x and y morphometric landmark coordinates. The resulting bare footprints data generated during this project was subsequently used to establish a database in Microsoft Access Database (MAD) to allow the data to be stored and new data to be added in, for future research work

Geomatics and Forensic: Progress and Challenges

Since graphics hold qualitative and quantitative information of complex crime scenes, it becomes a basic key to develop hypothesis in police investigations and also to prove these hypotheses in court. Forensic analysis involves tasks of scene information mining as well as its reconstruction in order to extract elements for explanatory police test or to show forensic evidence in legal proceedings. Currently, the combination of sensors and technologies allows the integration of spatial data and the generation of virtual infographic products (orthoimages, solid images, point clouds, cross‐sections, etc.) which are extremely attractive. These products, which successfully retain accurate 3D metric information, are revolutionizing dimensional reconstruction of objects and crime scenes. Thus, it can be said that the reconstruction and 3D visualization of complex scenes are one of the main challenges for the international scientific community. To overcome this challenge, techniques related with computer vision, computer graphics and geomatics work closely. This chapter reviews a set of geomatic techniques, applied to improve infographic forensic products, and its evolution. The integration of data from different sensors whose final purpose is 3D accurate modelling is also described. As we move into a highly active research area, where there are still many uncertainties to be resolved, the final section addresses these challenges and outlines future perspectives