45 research outputs found

    Walk This Way: Footwear Recognition Using Images & Neural Networks

    Get PDF
    Footwear prints are one of the most commonly recovered in criminal investigations. They can be used to discover a criminal's identity and to connect various crimes. Nowadays, footwear recognition techniques take time to be processed due to the use of current methods to extract the shoe print layout such as platter castings, gel lifting, and 3D-imaging techniques. Traditional techniques are prone to human error and waste valuable investigative time, which can be a problem for timely investigations. In terms of 3D-imaging techniques, one of the issues is that footwear prints can be blurred or missing, which renders their recognition and comparison inaccurate by completely automated approaches. Hence, this research investigates a footwear recognition model based on camera RGB images of the shoe print taken directly from the investigation site to reduce the time and cost required for the investigative process. First, the model extracts the layout information of the evidence shoe print using known image processing techniques. The layout information is then sent to a hierarchical network of neural networks. Each layer of this network is examined in an attempt to process and recognize footwear features to eliminate and narrow down the possible matches until returning the final result to the investigator

    The Law of Forensics: a proof beyond the shadow of doubt

    Get PDF
    This book gives an understanding of the application of forensic sciences to the law. It covers the crime scene investigation process, and provides an overview of the various kinds of forensic evidence that may be collected and presented in court. Points out the identification, documentation and collection of physical evidence, including fingerprints, shoe impressions, hair fibers, firearms evidence and questioned documents, It considers biological evidence, including DNA, and tries to analyse the scientific unimpeachablity of DNA, blood spatter and other fluids, forensic anthropology and odontology. Finally, the book engages fire investigation and forensic accounting. It is designed to provide a foundation in the field of criminology who are interested in the use of science and law to solve crime, and considers the impact of television and other media on the field of Forensic Science and the courtroom

    Statistical Assessment of the Significance of Fracture Fits in Trace Evidence

    Get PDF
    Fracture fits are often regarded as the highest degree of association of trace materials due to the common belief that inherently random fracturing events produce individualizing patterns. Often referred to as physical matches, fracture matches, or physical fits, these assessments consist of the realignment of two or more items with distinctive features and edge morphologies to demonstrate they were once part of the same object. Separated materials may provide a valuable link between items, individuals, or locations in forensic casework in a variety of criminal situations. Physical fit examinations require the use of the examiner’s judgment, which rarely can be supported by a quantifiable uncertainty or vastly reported error rates. Therefore, there is a need to develop, validate, and standardize fracture fit examination methodology and respective interpretation protocols. This research aimed to develop systematic methods of examination and quantitative measures to assess the significance of trace evidence physical fits. This was facilitated through four main objectives: 1) an in-depth review manuscript consisting of 112 case reports, fractography studies, and quantitative-based studies to provide an organized summary establishing the current physical fit research base, 2) a pilot inter-laboratory study of a systematic, score-based technique previously developed by our research group for evaluation of duct tape physical fit pairs and referred as the Edge Similarity Score (ESS), 3) the initial expansion of ESS methodology into textile materials, and 4) an expanded optimization and evaluation study of X-ray Fluorescence (XRF) Spectroscopy for electrical tape backing analysis, for implementation in an amorphous material of which physical fits may not be feasible due to lack of distinctive features. Objective 1 was completed through a large-scale literature review and manuscript compilation of 112 fracture fit reports and research studies. Literature was evaluated in three overall categories: case reports, fractography or qualitative-based studies, and quantitative-based studies. In addition, 12 standard operating protocols (SOP) provided by various state and federal-level forensic laboratories were reviewed to provide an assessment of current physical fit practice. A review manuscript was submitted to Forensic Science International and has been accepted for publication. This manuscript provides for the first time, a literature review of physical fits of trace materials and served as the basis for this project. The pilot inter-laboratory study (Objective 2) consisted of three study kits, each consisting of 7 duct tape comparison pairs with a ground truth of 4 matching pairs (3 of expected M+ qualifier range, 1 of the more difficult M- range) and 3 non-matching pairs (NM). The kits were distributed as a Round Robin study resulting in 16 overall participants and 112 physical fit comparisons. Prior to kit distribution, a consensus on each sample’s ESS was reached between 4 examiners with an agreement criterion of better than ± 10% ESS. Along with the physical comparison pairs, the study iii included a brief, post-study survey allowing the distributors to receive feedback on the participants’ opinions on method ease of use and practicality. No misclassifications were observed across all study kits. The majority (86.6%) of reported ESS scores were within ± 20 ESS compared to consensus values determined before the administration of the test. Accuracy ranged from 88% to 100%, depending on the criteria used for evaluation of the error rates. In addition, on average, 77% of ESS attributed no significant differences from the respective pre-distribution, consensus mean scores when subjected to ANOVA-Dunnett’s analysis using the level of difficulty as blocking variables. These differences were more often observed on sets of higher difficulty (M-, 5 out of 16 participants, or 31%) than on lower difficulty sets (M+ or M-, 3 out of 16 participants, or 19%). Three main observations were derived from the participant results: 1) overall good agreement between ESS reported by examiners was observed, 2) the ESS score represented a good indicator of the quality of the match and rendered low percent of error rates on conclusions 3) those examiners that did not participate in formal method training tended to have ESS falling outside of expected pre-distribution ranges. This interlaboratory study serves as an important precedent, as it represents the largest inter-laboratory study ever reported using a quantitative assessment of physical fits of duct tapes. In addition, the study provides valuable insights to move forward with the standardization of protocols of examination and interpretation. Objective 3 consisted of a preliminary study on the assessment of 274 total comparisons of stabbed (N=100) and hand-torn (N=174) textile pairs as completed by two examiners. The first 74 comparisons resulted in a high incidence of false exclusions (63%) on textiles prone to distortion, revealing the need to assess suitability prior to physical fit examination of fabrics. For the remaining dataset, five clothing items were subject to fracture of various textile composition and construction. The overall set consisted of 100 comparison pairs, 20 per textile item, 10 each per separation method of stabbed or hand-torn fractured edges, each examined by two analysts. Examiners determined ESS through the analysis of 10 bins of equal divisions of the total fracture edge length. A weighted ESS was also determined with the addition of three optional weighting factors per bin due to the continuation of a pattern, separation characteristics (i.e. damage or protrusions/gaps), or partial pattern fluorescence across the fractured edges. With the addition of a weighted ESS, a rarity ratio was determined as the ratio between the weighted ESS and non-weighted ESS. In addition, the frequency of occurrence of all noted distinctive characteristics leading to the addition of a weighting factor by the examiner was determined. Overall, 93% accuracy was observed for the hand-torn set while 95% accuracy was observed for the stabbed set. Higher misclassification in the hand-torn set was observed in textile items of either 100% polyester composition or jersey knit construction, as higher elasticity led to greater fracture edge distortion. In addition, higher misclassification was observed in the stabbed set for those textiles of no pattern as the stabbed edges led to straight, featureless bins often only associated due to pattern continuation. The results of this study are anticipated to provide valuable knowledge for the future development of protocols for evaluation of relevant features of textile fractures and assessments of the suitability for fracture fit comparisons. Finally, the XRF methodology optimization and evaluation study (Objective 4) expanded upon our group’s previous discrimination studies by broadening the total sample set of characterized iv tapes and evaluating the use of spectral overlay, spectral contrast angle, and Quadratic Discriminant Analysis (QDA) for the comparison of XRF spectra. The expanded sample set consisted of 114 samples, 94 from different sources, and 20 from the same roll. Twenty sections from the same roll were used to assess intra-roll variability, and for each sample, replicate measurements on different locations of the tape were analyzed (n=3) to assess the intra-sample variability. Inter-source variability was evaluated through 94 rolls of tapes of a variety of labeled brands, manufacturers, and product names. Parameter optimization included a comparison of atmospheric conditions, collection times, and instrumental filters. A study of the effects of adhesive and backing thickness on spectrum collection revealed key implications to the method that required modification to the sample support material Figures of merit assessed included accuracy and discrimination over time, precision, sensitivity, and selectivity. One of the most important contributions of this study is the proposal of alternative objective methods of spectral comparisons. The performance of different methods for comparing and contrasting spectra was evaluated. The optimization of this method was part of an assessment to incorporate XRF to a forensic laboratory protocol for rapid, highly informative elemental analysis of electrical tape backings and to expand examiners’ casework capabilities in the circumstance that a physical fit conclusion is limited due to the amorphous nature of electrical tape backings. Overall, this work strengthens the fracture fit research base by further developing quantitative methodologies for duct tape and textile materials and initiating widespread distribution of the technique through an inter-laboratory study to begin steps towards laboratory implementation. Additional projects established the current state of forensic physical fit to provide the foundation from which future quantitative work such as the studies presented here must grow and provided highly sensitive techniques of analysis for materials that present limited fracture fit capabilities

    A database of two-dimensional images of footwear outsole impressions

    Get PDF
    Footwear outsole images were obtained from 150 pairs of used shoes. The motivation for constructing the database was to enable a statistical analysis of two-dimensional (2D) images of shoe outsoles, to understand within shoe (between replicate images of the same shoe) and between shoe variability, and to develop methods for the evaluation of forensic pattern evidence of shoeprints. Since we scanned the outsole of the used shoes, the images capture not only the outsole pattern design but also the marks that arise from wear and tear and that may help identify the shoe that made the impression. Each shoe in a pair was scanned five times, so that replicate images can be used to estimate within-shoe variability. In total, there are 1500 2D images in the database. The EverOS footwear scanner was used to capture the outsole of each shoe. The scanner detects the weight distribution of the person wearing the shoe when he or she steps on the scanning surface. It images the portions of the outsole that make contact with the scanning surface. The database is a useful resource for forensic scientists or for anybody else with an interest in image comparison. The database we describe, was constructed by researchers in the Center for Statistics and Applications in Forensic Evidence (CSAFE) at Iowa State University

    Statistical Evaluation of Randomly Acquired Characteristics on Outsoles with Implications Regarding Chance Co-Occurrence and Spatial Randomness

    Get PDF
    Footwear evidence holds tremendous forensic value, owing to its ability to formulate linkages between victims, suspects and scenes. Naturally, the strength of these linkages is a function of the perceived clarity, quality and rarity of class, subclass and randomly acquired characteristics (RACs), which are the fundamental outsole features used to formulate source associations. In order to reach a conclusion when performing a footwear comparison, forensic examiners must assign value to the observed similarities and differences that exist between questioned crime scene and test impressions. Embedded within this process is an evaluation of the random association between unrelated shoes as a function of both class and acquired wear characteristics. To date, weight of evidence within this space has been largely informed by the training and subjective casework experience accumulated by an examiner over the life of his or her career. In pursuit of supporting the foundational validity of this comparison process, this research sought to quantify the chance association of RACs on unrelated shoes and the spatial distribution of these features on outsoles, with the long-term goal of aiding weight of evidence assessments in forensic footwear examinations. Using a large-scale database of 1,300 unrelated outsoles, the position and shape of 72,306 RACs was investigated. Features with consistent position and shape-classification were pairwise compared and sorted using a numerical estimate of similarity. Based on this assessment, more than 91,000 of the most quantitatively similar features were visually evaluated in order to model the relationship between numerical similarity and visual indistinguishability. Using this model, more than 1 million additional feature comparisons were evaluated in order to predict the potential for visual confusion. Subsequently, empirical and modeled probabilities of indistinguishability were combined with the chance for positional overlap to yield location- and shape-specific estimates of chance association. The results indicated that RACs exhibit high discriminating potential, with median chance associations ranging from 1 in 541,276 to 1 in 18,031,824, depending upon shape. However, additional inspection revealed that chance association was not constant across an outsole. Given this secondary observation, the spatial distribution of RACs on outsoles was further investigated. In order to conduct this analysis, a set of over 1.7 million null and 1.9 million alternative contact-modified synthetic distributions were simulated for comparison against the collected empirical data. Results indicated that Poisson null distributions (both synthetic and modeled) well-describe the frequency of RACs across approximately 64% of an outsole. Moreover, the regions not well represented by a random distribution were highly localized to three general areas (ball of the toe, arch, and edge of the heel). Based upon this observation, it was purported that an important theoretical or practical factor was additionally required to improve prediction in these locations. Therefore, spatial regression modeling was utilized in order to assess the impact of spatial effects on RAC distributions. Under optimal conditions, 87% of location-specific RAC counts were well predicted using contact area and incorporating neighboring cells’ data for contact and accidentals (a 67% performance increase over non-spatial predictions). Based upon a visual inspection of the remaining 13% of cells with persisting residual correlation, it was hypothesized that wear (the intersection of contact and use) may further improve model predictions and a proof of concept study was conducted to evaluate this theory. After incorporation of contact-localized wear as a predictor in the spatial models, nearly 96% of the outsole was well described. Considered collectively, the results from this work indicate that RACs are sufficiently rare, owing to variability in position, shape, and geometry, to differentiate shoes, as evidenced by the low probabilities of stochastic chance association between unrelated features. Furthermore, the majority of feature frequencies across the outsole can be adequately described by tread contact alone, irrespective of position. However, positional considerations for evidentiary value must be incorporated for features occurring in three specific areas including the ball of the toe, the arch, and the edge of the heel. Ultimately, the results from this study provide fundamental knowledge about the practical and theoretical/statistical factors that underpin the spatial distribution and subsequent weight of evidence of RACs for footwear evidence

    An algorithm to compare two‐dimensional footwear outsole images using maximum cliques and speeded‐up robust feature

    Get PDF
    Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect\u27s shoe. We propose a method for comparing two shoe outsole impressions that relies on robust features (speeded‐up robust feature; SURF) on each impression and aligns them using a maximum clique (MC). After alignment, an algorithm we denote MC‐COMP is used to extract additional features that are then combined into a univariate similarity score using a random forest (RF). We use a database of shoe outsole impressions that includes images from two models of athletic shoes that were purchased new and then worn by study participants for about 6 months. The shoes share class characteristics such as outsole pattern and size, and thus the comparison is challenging. We find that the RF implemented on SURF outperforms other methods recently proposed in the literature in terms of classification precision. In more realistic scenarios where crime scene impressions may be degraded and smudged, the algorithm we propose—denoted MC‐COMP‐SURF—shows the best classification performance by detecting unique features better than other methods. The algorithm can be implemented with the R‐package shoeprintr

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

    Get PDF
    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

    Deep Learning Analysis and Age Prediction from Shoeprints

    Full text link
    Human walking and gaits involve several complex body parts and are influenced by personality, mood, social and cultural traits, and aging. These factors are reflected in shoeprints, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait-pattern disorders, biometrics, and sport studies.Comment: 24 pages, 20 Figure

    Policy Implementation Analysis of District Health System to Improve Health Services: Study in North Central Timor Regency, East Nusa Tenggara Timur Province, Indonesis

    Get PDF
    Context: Improving degree of public health in a region requires quality health services. For this reason, district health system has been formed which can be implemented comprehensively to the target community. A study is needed to find out the factors that influence policy implementation so that quality of health services can be improved. This study used quantitative method with structural equation models to find patterns of the relationship between the district health system and health services. The results showed that there are 7 indicators that are part of the district health system factors, 2 indicators that are part of the resposivensss factor, 8 indicators that are part of the policy implementation factor, and 3 indicators that are part of the health service factor. These indicators have loading factor ≄ 0.5. The district health system consisting of 7 subsystems if properly implemented will have a positive impact on health services by 1.98. Contribution of policy implementation in improving health services will be great if the district health system is implemented together with responsiveness, so that the total effect becomes 2.20
    corecore