Probabilistic Fingermark Quality Assessment with Quality Region Localization

The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. Fingermark quality indicates value and utility of trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. Deposition of fingermarks on random surfaces occurs spontaneously in uncontrolled fashion, which introduces imperfections into the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for automated fingermark quality assessment (AFQA). We use modern deep learning techniques which have the ability to extract patterns even from noisy data, and combine them with ideas from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value, and if needed, the uncertainty of the model. Additionally, we complement the predicted quality value with a corresponding quality map. We use GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image.

OBLAK Tim;  HARAKSIM Rudolf;  BESLAY Laurent;  PEER Peter; 

2023-04-18

MDPI

JRC133058

1424-8220 (online),   

https://www.mdpi.com/journal/sensors,    https://publications.jrc.ec.europa.eu/repository/handle/JRC133058,   

10.3390/s23084006 (online),