Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application
This paper presents the design of a system to support the identification of victims of disasters and terrorist attacks. The system, called ID Victim (IDV), is a web application using a mobile app and data server. The DVI (Disaster Victim Identification) procedure, an international standard developed...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/727 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589204086849536 |
|---|---|
| author | Zbigniew Piotrowski Marta Bistroń Gabriel Jekateryńczuk Paweł Kaczmarek Dymitr Pietrow |
| author_facet | Zbigniew Piotrowski Marta Bistroń Gabriel Jekateryńczuk Paweł Kaczmarek Dymitr Pietrow |
| author_sort | Zbigniew Piotrowski |
| collection | DOAJ |
| description | This paper presents the design of a system to support the identification of victims of disasters and terrorist attacks. The system, called ID Victim (IDV), is a web application using a mobile app and data server. The DVI (Disaster Victim Identification) procedure, an international standard developed by Interpol, is used. The purpose of the IDV system is to facilitate and expedite the process of determining victims’ identities. A neural identification module was developed and trained on approximately 13,000 images from the LFW dataset and fine-tuned using 400 simulated PostMortem (PM) and AnteMortem (AM) images. Postmortem data include photographs of victims while antemortem data consist of pre-disaster photos of potential victims. The module generates a hypothesis, linking PM to AM, which is then verified. The module achieved test identification accuracy of up to 60% for 25 sample PM and AM sets. The system partially automates photo comparisons by DVI teams, improving efficiency, reducing identification time, and limiting the exposure of operators to graphic images. Implementing the system as a mobile application accelerates the process by enabling direct data entry during victim examinations on-site. |
| format | Article |
| id | doaj-art-9f5b6156d46940d3b0430800e1734ff9 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9f5b6156d46940d3b0430800e1734ff92025-01-24T13:20:36ZengMDPI AGApplied Sciences2076-34172025-01-0115272710.3390/app15020727Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile ApplicationZbigniew Piotrowski0Marta Bistroń1Gabriel Jekateryńczuk2Paweł Kaczmarek3Dymitr Pietrow4Institute of Communication Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, PolandInstitute of Communication Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, PolandInstitute of Communication Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, PolandInstitute of Communication Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, PolandInstitute of Communication Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, PolandThis paper presents the design of a system to support the identification of victims of disasters and terrorist attacks. The system, called ID Victim (IDV), is a web application using a mobile app and data server. The DVI (Disaster Victim Identification) procedure, an international standard developed by Interpol, is used. The purpose of the IDV system is to facilitate and expedite the process of determining victims’ identities. A neural identification module was developed and trained on approximately 13,000 images from the LFW dataset and fine-tuned using 400 simulated PostMortem (PM) and AnteMortem (AM) images. Postmortem data include photographs of victims while antemortem data consist of pre-disaster photos of potential victims. The module generates a hypothesis, linking PM to AM, which is then verified. The module achieved test identification accuracy of up to 60% for 25 sample PM and AM sets. The system partially automates photo comparisons by DVI teams, improving efficiency, reducing identification time, and limiting the exposure of operators to graphic images. Implementing the system as a mobile application accelerates the process by enabling direct data entry during victim examinations on-site.https://www.mdpi.com/2076-3417/15/2/727computer visionCNN module compilationdeep metric learningdisaster victim verification |
| spellingShingle | Zbigniew Piotrowski Marta Bistroń Gabriel Jekateryńczuk Paweł Kaczmarek Dymitr Pietrow Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application Applied Sciences computer vision CNN module compilation deep metric learning disaster victim verification |
| title | Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application |
| title_full | Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application |
| title_fullStr | Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application |
| title_full_unstemmed | Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application |
| title_short | Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application |
| title_sort | victim verification with the use of deep metric learning in dvi system supported by mobile application |
| topic | computer vision CNN module compilation deep metric learning disaster victim verification |
| url | https://www.mdpi.com/2076-3417/15/2/727 |
| work_keys_str_mv | AT zbigniewpiotrowski victimverificationwiththeuseofdeepmetriclearningindvisystemsupportedbymobileapplication AT martabistron victimverificationwiththeuseofdeepmetriclearningindvisystemsupportedbymobileapplication AT gabrieljekaterynczuk victimverificationwiththeuseofdeepmetriclearningindvisystemsupportedbymobileapplication AT pawełkaczmarek victimverificationwiththeuseofdeepmetriclearningindvisystemsupportedbymobileapplication AT dymitrpietrow victimverificationwiththeuseofdeepmetriclearningindvisystemsupportedbymobileapplication |