Carriquiry, Alicia

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Email Address
alicia@iastate.edu
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Title
Distinguished Professor
Academic or Administrative Unit
Organizational Unit
Statistics
As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
Organizational Unit
Statistics

The Department of Statistics seeks to teach students in the theory and methodology of statistics and statistical analysis, preparing its students for entry-level work in business, industry, commerce, government, or academia.

History
The Department of Statistics was formed in 1948, emerging from the functions performed at the Statistics Laboratory. Originally included in the College of Sciences and Humanities, in 1971 it became co-directed with the College of Agriculture.

Dates of Existence
1948-present

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https://dr.lib.iastate.edu/handle/20.500.12876/100179
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Evaluating Reference Sets for Score-Based Likelihood Ratios for Camera Device Identification

2022-08-05 , Reinders, Stephanie , Ommen, Danica , Carriquiry, Alicia , Center for Statistics and Applications in Forensic Evidence , Statistics

An investigator wants to know if an illicit image captured by an unknown camera was taken by a person of interest’s (POI’s) phone. Score-based likelihood ratios (SLRs) have been used to answer this question in previous research. We explore whether the reference set used to calculate SLRs makes a difference in the outcome when the questioned image comes from a phone of the same model as the POI’s phone.

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A New Algorithm for Source Identification of Look-alike Footwear Impressions Based on Automatic Alignment

2022-08-04 , Lee, Hana , Carriquiry, Alicia , Center for Statistics and Applications in Forensic Evidence , Statistics

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The effect of image descriptors on the performance of classifiers of footwear outsole image pairs

2022-2 , Park, Soyoung , Carriquiry, Alicia , Statistics , Center for Statistics and Applications in Forensic Evidence

Shoe prints are commonly found at the scene of a crime and can sometimes help link a suspect to the scene. Because prints tend to be partially observed or smudgy, comparing crime scene prints with reference images from a putative shoe can be challenging. Footwear examiners rely on guidelines such as those published by SWGTREAD [1] to visually assess the similarity between two or more footwear impressions, one reason being that reliable, quantitative methods have yet to be validated for use in real cases. To help in the development of such methods, we created a study dataset of images of outsole impressions that shared class characteristics and degree of wear and that were subject to a specific type of degradation. We also propose a method to quantify the similarity between two outsole images that extends the capabilities of MC-COMP [2]. The proposed method is composed of three steps; (1) extracting image descriptors, (2) aligning images using the maximum clique, (3) calculating similarity values using two different classifiers; (a) degree of overlap between the two images, and (b) a score produced by a random forest. To explore the performance of the algorithm we propose, we compared degraded, crime scene-like images to high-quality reference images produced by the same or by different shoes. Even though comparisons involved matches or very close non-matches, and one of the images was blurry, the algorithm shows good source classification performance.

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Forged Signature Recognition using Hotelling's T2 Analysis

2022 , Allen, Emily , Carriquiry, Alicia , Ommen, Danica , Center for Statistics and Applications in Forensic Evidence , Statistics

Can we tell whether a signature is authentic or forged? To address this question, I am analyzing data obtained with a system called MovAlyzeR. The MovAlyzeR system records various information about a signature as it is written. The signatures are decomposed into strokes, and each stroke’s qualities are analyzed. The variables collected include size, position, straightness, duration, velocity, jerk, pressure, and time to peak velocity. I will examine each of the variables using a Hotelling’s T Squared Test and determine whether they help differentiate an authentic signature from a forged one. This analysis will be performed using the program R. The Hotelling’s T Squared test will generate a probability that two handwriting samples may have the same means for each variable tested. If it is a possibility that the samples have the same means, we cannot reject the hypothesis that the two samples of handwriting came from the same writer and that the signature is authentic. However, if the sample means are different, it can be concluded that the two handwriting samples did not come from the same writer and that one was forged. This analysis helps identify forgery and can supplement court cases that involve the crime of forgery.

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Comparing handwriterand FLASH ID®, Two Handwriting Analysis Programs

2022-08-04 , Reinders, Stephanie , Ommen, Danica , Carriquiry, Alicia , Center for Statistics and Applications in Forensic Evidence

FLASH ID and handwriter are computer programs that compare questioned handwritten documents against handwritten samples from known writers. FLASH ID was developed by Sciometrics and is used by the FBI, Handwriter is an open-source R package designed by CSAFE. We compare the accuracy of handwriter and FLASH ID on a closed set of writers using reference handwriting samples from the CSAFE, CVL, and IAM handwriting databases.

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Center for Statistics and Applications in Forensic Evidence Update

2022-07 , Carriquiry, Alicia , Center for Statistics and Applications in Forensic Evidence

The information below highlights a sample of current research initiatives led by the CSAFE team. Additional accomplishments in other forensic science disciplines will be discussed in subsequent issues of Forensic Science Review. Visit the CSAFE website www.forensicstats.org to learn more about our research and educational opportunities. CSAFE advancements are founded on strong collaborations with the forensic science community. If you would like to partner with CSAFE, please contact us at www.forensicstats.org/contact.

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Publication

Effect of Weight on Outsole Impressions

2022 , Maier, Katarina , Chu, David , Lee, Hana , Han, Valerie , Carriquiry, Alicia , Center for Statistics and Applications in Forensic Evidence , Statistics

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Revisiting the Likelihood Ratio: A Gentle Introduction and Some Examples

2022-08-04 , Carriquiry, Alicia , Center for Statistics and Applications in Forensic Evidence , Statistics

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Score-Based Likelihood Ratios for Camera Device Identification Using Cameras of the Same Brand for the Alternative Device Population

2022-02-24 , Reinders, Stephanie , Ommen, Danica , Martin, Abby , Carriquiry, Alicia , Center for Statistics and Applications in Forensic Evidence

Score-based likelihood ratios are a statistical method for quantifying the weight of evidence and have been used in many areas of forensics, including camera device identification1,2,3. Small sensor imperfections caused during manufacturing, called photo response non-uniformity4, leave identifying features, called a camera fingerprint, in the images that a camera takes. The sample correlation measures the similarity (or dissimilarity) between the camera fingerprint from the person of interest’s camera and the camera fingerprint in the questioned image. On its own, it is difficult to know how to interpret this score. Is a score of 0.25 evidence that the questioned image originated from the person of interest’s camera? What about a score of 0.5? To make sense of the score, it is compared with two different reference sets of scores: matching and non-matching. Matching scores are sample correlations between two fingerprints known to come from the person of interest’s camera. Non-matching scores are sample correlations between two fingerprints known to come from two different cameras. An alternative set of cameras that does not include the person of interest’s camera is used to build the set of non-matching scores. It turns out, that researchers have not agreed upon a best method for constructing the alternative population for score-based likelihood ratios5,6. Recently, researchers calculated score-based likelihood ratios for camera device identification using 48 cameras representing 26 models7. This present research explores whether the rates of misleading evidence can be decreased by restricting the alternative device population to cameras of the same brand as the person of interest’s camera.

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Publication

Quantifying Common Word Variance Through Rainbow Triangle Graph Decomposition of the Common Word 'the'

2022 , Arabio, Alexandra , Carriquiry, Alicia , Ommen, Danica , Center for Statistics and Applications in Forensic Evidence , Statistics

Handwriting comparative analysis has recently been criticized based off of the subjective nature of traditional examinations. In an attempt to support traditional examination with objective measures, this project provides results from a study where features of handwriting are examined through point decomposition and rainbow triangulation. Using this method to examine handwriting samples, more specific information can be obtained from each exemplar and is able to be standardized to be compared both within a writer and between different writers. Triangles are able to provide information on angles, edge slopes, edge lengths, and areas that all prove useful for quantitative analysis and when trying to compare triangles in terms of similarity and possible congruency or similarity. By forming rainbow triangles over these samples, it is possible to gauge the variation within a single writer and to compare these quantitative values to other samples of unknown sources. Using this information, the study aims to form a quantitative analysis of handwriting samples and to calculate how similar or dissimilar two samples are from one another. One of the study’s main goals is to form these triangles from multiple samples from several different writers and to group, identify, and accurately determine what samples came from which writer. Finally, multiple summary statistics are explored to determine whether any can be used to discriminate between inclusions and exclusions using data where ground truth is known such as a true match. This project hopes to impact the forensic community by demonstrating a new method for analyzing handwriting that could be used in junction with current practices to be able to better quantify and support results regarding sources.

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