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IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...

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Updated: Aug 1, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

Probabilistic Fingermark Quality Assessment with Quality Region Localisation.

Tim Oblak^1,2, Rudolf Haraksim², Laurent Beslay²

¹Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

This summary is machine-generated.

This study introduces a new automated system for assessing fingermark (latent fingerprint) quality using deep learning and explainable AI. The framework enhances forensic investigations by providing reliable quality scores and visual maps for trace evidence.

Keywords:

biometrics deep learning explainability fingermark forensic latent fingerprint probabilistic interpretation quality assessment quality map

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Area of Science:

Forensic Science
Artificial Intelligence
Computer Vision

Background:

Fingermark quality assessment is crucial in forensic investigations, influencing evidence processing and database matching.
Fingermark deposition on surfaces often results in imperfections, complicating analysis.
Existing methods may lack transparency and robust quality evaluation.

Purpose of the Study:

To develop a probabilistic framework for Automated Fingermark Quality Assessment (AFQA).
To enhance the transparency and interpretability of fingermark quality predictions.
To correlate quality assessment with forensic evidence utility.

Main Methods:

Utilized deep learning techniques for pattern extraction from noisy fingermark data.
Integrated eXplainable AI (XAI) methodologies for model transparency.
Employed GradCAM for generating quality maps and identifying influential regions.

Main Results:

Achieved high regression performance in predicting fingermark quality.
Demonstrated improved interpretability and transparency of the AFQA model.
Showed quality maps correlate strongly with minutiae point density.

Conclusions:

The proposed AFQA framework offers a reliable and transparent method for evaluating fingermark quality.
The system aids forensic investigators by providing actionable insights into trace evidence.
Deep learning combined with XAI advances the field of automated forensic analysis.