{"title":"加强法医鞋印分析:Shoe-MS算法在挑战证据中的应用","authors":"Moonsoo Jang , Alicia Carriquiry , Soyoung Park","doi":"10.1016/j.scijus.2025.101255","DOIUrl":null,"url":null,"abstract":"<div><div>Quantitative assessment of pattern evidence is a challenging task, particularly in the context of forensic investigations where the accurate identification of sources and classification of items in evidence are critical. Emerging deep learning approaches can become useful tools for examiners responsible for pattern recognition and analysis. This paper explores the Shoe-MS algorithm, a deep learning-based framework specifically designed for forensic footwear analysis where the input consists of two paired images, and the output is an estimated similarity score that takes on a value between zero and one. We implement Shoe-MS on two different databases that permit assessing the algorithm’s performance for source identification and for the classification of degraded images. Our experimental results demonstrate that the Shoe-MS algorithm achieves high performance across both tasks, highlighting its potential for forensic footwear analysis. No algorithm can substitute examiners, but Shoe-MS produces reliable similarity scores and can help examiners make probabilistic, reproducible, and repeatable assessments. Initial findings suggest that Shoe-MS can be a valuable tool for examiners evaluating pattern evidence, especially when crime scene images are not of the highest quality.</div></div>","PeriodicalId":49565,"journal":{"name":"Science & Justice","volume":"65 4","pages":"Article 101255"},"PeriodicalIF":1.9000,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing forensic shoeprint analysis: Application of the Shoe-MS algorithm to challenging evidence\",\"authors\":\"Moonsoo Jang , Alicia Carriquiry , Soyoung Park\",\"doi\":\"10.1016/j.scijus.2025.101255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Quantitative assessment of pattern evidence is a challenging task, particularly in the context of forensic investigations where the accurate identification of sources and classification of items in evidence are critical. Emerging deep learning approaches can become useful tools for examiners responsible for pattern recognition and analysis. This paper explores the Shoe-MS algorithm, a deep learning-based framework specifically designed for forensic footwear analysis where the input consists of two paired images, and the output is an estimated similarity score that takes on a value between zero and one. We implement Shoe-MS on two different databases that permit assessing the algorithm’s performance for source identification and for the classification of degraded images. Our experimental results demonstrate that the Shoe-MS algorithm achieves high performance across both tasks, highlighting its potential for forensic footwear analysis. No algorithm can substitute examiners, but Shoe-MS produces reliable similarity scores and can help examiners make probabilistic, reproducible, and repeatable assessments. Initial findings suggest that Shoe-MS can be a valuable tool for examiners evaluating pattern evidence, especially when crime scene images are not of the highest quality.</div></div>\",\"PeriodicalId\":49565,\"journal\":{\"name\":\"Science & Justice\",\"volume\":\"65 4\",\"pages\":\"Article 101255\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science & Justice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1355030625000395\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, LEGAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science & Justice","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1355030625000395","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
Enhancing forensic shoeprint analysis: Application of the Shoe-MS algorithm to challenging evidence
Quantitative assessment of pattern evidence is a challenging task, particularly in the context of forensic investigations where the accurate identification of sources and classification of items in evidence are critical. Emerging deep learning approaches can become useful tools for examiners responsible for pattern recognition and analysis. This paper explores the Shoe-MS algorithm, a deep learning-based framework specifically designed for forensic footwear analysis where the input consists of two paired images, and the output is an estimated similarity score that takes on a value between zero and one. We implement Shoe-MS on two different databases that permit assessing the algorithm’s performance for source identification and for the classification of degraded images. Our experimental results demonstrate that the Shoe-MS algorithm achieves high performance across both tasks, highlighting its potential for forensic footwear analysis. No algorithm can substitute examiners, but Shoe-MS produces reliable similarity scores and can help examiners make probabilistic, reproducible, and repeatable assessments. Initial findings suggest that Shoe-MS can be a valuable tool for examiners evaluating pattern evidence, especially when crime scene images are not of the highest quality.
期刊介绍:
Science & Justice provides a forum to promote communication and publication of original articles, reviews and correspondence on subjects that spark debates within the Forensic Science Community and the criminal justice sector. The journal provides a medium whereby all aspects of applying science to legal proceedings can be debated and progressed. Science & Justice is published six times a year, and will be of interest primarily to practising forensic scientists and their colleagues in related fields. It is chiefly concerned with the publication of formal scientific papers, in keeping with its international learned status, but will not accept any article describing experimentation on animals which does not meet strict ethical standards.
Promote communication and informed debate within the Forensic Science Community and the criminal justice sector.
To promote the publication of learned and original research findings from all areas of the forensic sciences and by so doing to advance the profession.
To promote the publication of case based material by way of case reviews.
To promote the publication of conference proceedings which are of interest to the forensic science community.
To provide a medium whereby all aspects of applying science to legal proceedings can be debated and progressed.
To appeal to all those with an interest in the forensic sciences.