Detection of latent fingerprints using high-resolution 3D confocal microscopy in non-planar acquisition scenarios

Stefan Kirst; Claus Vielhauer

doi:10.1117/12.2081182

4 March 2015 Detection of latent fingerprints using high-resolution 3D confocal microscopy in non-planar acquisition scenarios

Stefan Kirst, Claus Vielhauer

Author Affiliations +

Proceedings Volume 9409, Media Watermarking, Security, and Forensics 2015; 94090D (2015) https://doi.org/10.1117/12.2081182
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

In digitized forensics the support of investigators in any manner is one of the main goals. Using conservative lifting methods, the detection of traces is done manually. For non-destructive contactless methods, the necessity for detecting traces is obvious for further biometric analysis. High resolutional 3D confocal laser scanning microscopy (CLSM) grants the possibility for a detection by segmentation approach with improved detection results. Optimal scan results with CLSM are achieved on surfaces orthogonal to the sensor, which is not always possible due to environmental circumstances or the surface's shape. This introduces additional noise, outliers and a lack of contrast, making a detection of traces even harder. Prior work showed the possibility of determining angle-independent classification models for the detection of latent fingerprints (LFP). Enhancing this approach, we introduce a larger feature space containing a variety of statistical-, roughness-, color-, edge-directivity-, histogram-, Gabor-, gradient- and Tamura features based on raw data and gray-level co-occurrence matrices (GLCM) using high resolutional data. Our test set consists of eight different surfaces for the detection of LFP in four different acquisition angles with a total of 1920 single scans. For each surface and angles in steps of 10, we capture samples from five donors to introduce variance by a variety of sweat compositions and application influences such as pressure or differences in ridge thickness. By analyzing the present test set with our approach, we intend to determine angle- and substrate-dependent classification models to determine optimal surface specific acquisition setups and also classification models for a general detection purpose for both, angles and substrates. The results on overall models with classification rates up to 75.15% (kappa 0.50) already show a positive tendency regarding the usability of the proposed methods for LFP detection on varying surfaces in non-planar scenarios.

Citation Download Citation

Stefan Kirst and Claus Vielhauer "Detection of latent fingerprints using high-resolution 3D confocal microscopy in non-planar acquisition scenarios", Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090D (4 March 2015); https://doi.org/10.1117/12.2081182

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available