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

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

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

Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently

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

Understanding the Novice Decision-Making Process in Forensic Footwear Examinations: Accuracy and Decision Rules

The reproducibility of experienced-based forensic pattern interpretation is founded on the notion that domain-specific knowledge can be successfully distributed and applied among experts within a group. This assumption persists, even when the examination is complicated by variations in case circumstances, such as impression clarity and totality, as well as media, substrate, collection mechanism and enhancement. While it is further theorized that many of these factors (as well as additional confounding factors) are at play during an examination, the manner and extent to which these sources of variability affect the examination of footwear evidence remain unclear. In order to explore this hypothesis, a data mining technique called dominance-based rough set approach (DRSA) was applied to characterize the novice examiners’ decision-making process, due to its ability to capture useful information from a set of hybrid data with latent preference orders and discover knowledge in the form of decision rules. Through this approach, two objectives were addressed: the identification of factors that affect footwear examination and conclusions within the novice group, and the evaluation of decision rule quality as a function of support, strength, certainty and lift factors. The results of the study showed that in general, novice examiners’ case assessments were found to be outside the acceptable conclusion range more than 50\% of the time, with general tendencies to assign ambiguous conclusions, such as ``limited association of class characteristics and ``lacks sufficient detail, rather than more definitive ones such as ``identification or ``exclusion. When assessments were further explored using DRSA, 23 decision rules were induced (13 \textit{certain} and 10 \textit{possible}). Of the 13 \textit{certain} rules, 75\% of the induced rules were dominated by the examiner’s background, rather than case attributes, and 50\% of the \textit{possible} rules indicated that media type was a prevalent factor in the examiners’ determination of similarity/dissimilarity, as they attempted to interpret media-substrate interaction and reconcile this interpretation with SWGTREAD conclusion guidelines. Only when examiner attributes were excluded from the analysis, forcing the induction of rules based on case attributes only, did case-based features become prominent, but only with very low rule-support. In the second phase of work related to this project, the nature and type of rules induced based on expert assessments will be examined and compared to those generated from this novice set in order to compare and interpret the manner in which domain-specific knowledge dominates induced rules

Quantifying the uniqueness of footwear impressions from the same footwear source

The field of impression evidence analysis employs the concept of uniqueness, in order to arrive at a conclusion of association, between an evidence and a reference sample. However, the idea of uniqueness and its application in this field of forensic science, is considered conceptual by a few practitioners and courts, as it fails to be supported by sound scientific studies and experimentation. This study aims to provide a scientific basis for the idea of uniqueness and its validity in footwear impression evidence analysis. Additionally, this study also aims to determine the presence or the lack of, a variation, between outsole impression size and the outsole size of the footwear that created the impression. The study required 3 volunteers to each, create 30 touch impressions and 10 step impressions with the same footwear, utilizing the EZID^TM Footwear Impression System manufactured by Sirchie®. Using Adobe Photoshop^TM CS4, the 30 touch impressions were analyzed by sequentially overlaying the impressions onto each other, in an effort to determine the area of overlay agreement between the impressions being analyzed. Subsequently, the 10 step impressions were examined by measuring the size of the impression created on the EZID Impression test cards, from the toe area to the heel area of the outsole impression. The data obtained indicate that, even under controlled settings and while attempting to maintain constant pressure through the creation of footwear impressions, it is impossible to generate prints that overlay perfectly. Further, the data also shows that a negligible variation occurs between the footwear outsole length and the length of the impression it creates, using the EZID^TM Footwear Impression System. This variation, however, is too small to cause any major hindrances in the estimation of footwear size

Empirical Evaluation of the Reliability of Photogrammetry Software in the Recovery of Three-Dimensional Footwear Impressions.

This paper examines the reliability of Structure from Motion (SfM) photogrammetry as a tool in the capture of forensic footwear marks. This is applicable to photogrammetry freeware DigTrace but is equally relevant to other SfM solutions. SfM simply requires a digital camera, a scale bar, and a selection of oblique photographs of the trace in question taken at the scene. The output is a digital three-dimensional point cloud of the surface and any plastic trace thereon. The first section of this paper examines the reliability of photogrammetry to capture the same data when repeatedly used on one impression, while the second part assesses the impact of varying cameras. Using cloud to cloud comparisons that measure the distance between two-point clouds, we assess the variability between models. The results highlight how little variability is evident and therefore speak to the accuracy and consistency of such techniques in the capture of three-dimensional traces. Using this method, 3D footwear impressions can, in many substrates, be collected with a repeatability of 97% with any variation between models less than ~0.5 mm

DTW-Radon-based Shape Descriptor for Pattern Recognition

International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion

Cross-Domain Image Matching with Deep Feature Maps

We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene. This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on hard surfaces to impressions made in soil) and the lack of comprehensive databases of shoe outsole tread patterns. We find that mid-level features extracted by pre-trained convolutional neural nets are surprisingly effective descriptors for this specialized domains. However, the choice of similarity measure for matching exemplars to a query image is essential to good performance. For matching multi-channel deep features, we propose the use of multi-channel normalized cross-correlation and analyze its effectiveness. Our proposed metric significantly improves performance in matching crime scene shoeprints to laboratory test impressions. We also show its effectiveness in other cross-domain image retrieval problems: matching facade images to segmentation labels and aerial photos to map images. Finally, we introduce a discriminatively trained variant and fine-tune our system through our proposed metric, obtaining state-of-the-art performance