Development of Machine Learning assisted Fingermark Imaging Software (iFIS)
June 2026, ASMS Conference
Simona Francese1; Elias Jensen2; Sara Tortorella3; Chloe Spencer1, 4; Giuseppe Arturi5; Simon Cross6; Hassan Ugail2
1Sheffield Hallam University, Sheffield, United Kingdom; 2University of Bradford, Bradford, United Kingdom; 3Mass Analytica, Sant Cugat del Valles, Spain; 4University of Nottingham, Nottingham, United Kingdom; 5Molecular Discovery, Borehamwood, United Kingdom; 6Mass Analytica, S.L., Borehamwood, United Kingdom
Abstract
Introduction
Molecular fingerprinting has been featured in the Fingermark Visualisation Manual edited by Dstl/Home Office and is being used in Police casework. It encompasses the application of Mass Spectrometry Imaging (MSI), particularly MALDI MSI, for the provision of biometric information, through generating multiple molecular images of crime scene fingermark evidence, alongside contextual (molecular) information. Whilst both advanced freeware and proprietary MSI exist, they are complex as mostly built to process biological tissue imaging data. We have developed a dedicated software, enabling auditable fingermark images “manipulation”, seamless separation of overlapping fingermarks and, crucially, integrating a machine learning algorithm capable of grading and finding fingermark images of the highest quality within a seconds, without manually having to inspect the entire mass range.
Methods
Following Ethical approval (ER52762288), fingermarks were matrix spray-coated using the HTX M3+™ Sprayer (HTX Technologies, USA) and imaged on a SELECT SERIES MRT MALDI mass spectrometer (Waters Corporation, UK) in positive mode. Lipostar MSI (Molecular Discovery, UK) – used as the skeleton to build iFIS – generated 663 images which were graded according to the 5-point scale Scotland Yard system machine learning model. The ML algorithm utilised a range of deep features based on the Resnet50 architecture corresponding to the visual characteristics of the fingermark (minutiae and texture features). These were then fed to a Support Vector Machine-based classification algorithm for categorizing fingermarks into five distinct categories.
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