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ERIC Number: ED659553
Record Type: Non-Journal
Publication Date: 2024
Pages: 111
Abstractor: As Provided
ISBN: 979-8-3835-9408-7
ISSN: N/A
EISSN: N/A
Available Date: N/A
Computational Shoeprint Analysis for Forensic Science
Samia Shafique
ProQuest LLC, Ph.D. Dissertation, University of California, Irvine
Shoeprints are a common type of evidence found at crime scenes and are regularly used in forensic investigations. However, their utility is limited by the lack of reference footwear databases that cover the large and growing number of distinct shoe models. Additionally, existing methods for matching crime-scene shoeprints to reference databases cannot effectively employ deep learning techniques due to a lack of training data. Moreover, these methods typically rely on comparing crime-scene shoeprints with clean reference prints instead of more detailed tread depth maps. To address these challenges, we break down the problem into two parts. First, we leverage shoe tread images sourced from online retailers to predict their corresponding depth maps, which are then thresholded to generate prints, thus constructing a comprehensive reference database. Next, we use a section of this database to train a retrieval network that matches query crime-scene shoeprints to tread depth maps. Extensive experimentation across multiple datasets demonstrates the state-of-the-art performance achieved by both the database creation and retrieval steps, validating the effectiveness of our proposed methodology. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A