Publikationen

Autor	Hmouda, Yasser; Henniger, Olaf; Kuijper, Arjan
Datum	2025
Art	Conference Paper
Abstrakt	Fingermarks, also known as latent fingerprints, which can be found on crime scenes, are used by law enforcement agencies to identify suspects. Dactyloscopic experts compare fingermarks from a crime scene with fingerprints of suspects taken under controlled conditions and stored in forensic databases. Only fingermark images of adequate quality can result in a conclusive match. Machine-learning techniques assessing the quality of fingermark images can support the tedious and time-consuming work of forensic experts. We propose a random forest model that classifies fingermark images based on handcrafted features into two classes indicating whether the images are of value for identification or not. This helps to ensure a sufficient quality of fingermark images to be examined.
Konferenz	International Workshop on Biometrics and Forensics 2025
Projekt	Next Generation Biometric Systems
Url	https://publica.fraunhofer.de/handle/publica/490894

Fingermark Image Quality Assessment with Random-Forest Classifier