NDLI: On using flat bed scanners for the age determination of latent fingerprints: first results for the binary pixel feature

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On using flat bed scanners for the age determination of latent fingerprints: first results for the binary pixel feature

Content Provider	ACM Digital Library
Author	Merkel, Ronny Vielhauer, Claus
Abstract	Determining the age of latent fingerprints found at crime scenes is a very pressing, yet so far unresolved challenge in forensics. Recently, a first aging feature called binary pixel has been introduced, based on the loss of image contrast of fingerprint time series captured non-invasively with a Chromatic White Light (CWL) sensor. Such new feature seems to be promising, showing a logarithmic behavior for fingerprints when aging. However, a CWL sensor is very expensive, bulky and comparatively slow in the acquisition. We here explore the possibility of using a common flat bed scanner as a cheaper, more flexible and faster alternative to the CWL technique. Comparing 250 fingerprint time series scanned in regular time intervals over a period of 24 hours with a CWL sensor as well as a flat bed scanner (18000 scans in total), our conclusions are threefold: firstly, the binary pixel feature can reliably be produced with a flat bed scanner for scan images of sufficient quality according to our subjective quality metric defined in this paper. Secondly, however, only 24% of flat bed scanner images are of high quality, clearly showing the much higher reliability of CWL results. Thirdly, the experiments furthermore indicate that the binary pixel feature is not sensor specific and can be applied to different image sensors.
Starting Page	39
Ending Page	46
Page Count	8
File Format	PDF
ISBN	9781450314176
DOI	10.1145/2361407.2361415
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-09-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Binary pixel Flat bed scanner Age determination Chromatic white light sensor Latent fingerprints Crime scene forensics
Content Type	Text
Resource Type	Article

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