Determining the age of latent fingerprint traces found at crime scenes is an unresolved research issue since decades.
Solving this issue could provide criminal investigators with the specific time a fingerprint trace was left on a surface, and
therefore would enable them to link potential suspects to the time a crime took place as well as to reconstruct the sequence
of events or eliminate irrelevant fingerprints to ensure privacy constraints. Transferring imaging techniques from
different application areas, such as 3D image acquisition, surface measurement and chemical analysis to the domain of
lifting latent biometric fingerprint traces is an upcoming trend in forensics. Such non-destructive sensor devices might
help to solve the challenge of determining the age of a latent fingerprint trace, since it provides the opportunity to create
time series and process them using pattern recognition techniques and statistical methods on digitized 2D, 3D and chemical
data, rather than classical, contact-based capturing techniques, which alter the fingerprint trace and therefore make
continuous scans impossible.
In prior work, we have suggested to use a feature called binary pixel, which is a novel approach in the working field of
fingerprint age determination. The feature uses a Chromatic White Light (CWL) image sensor to continuously scan a
fingerprint trace over time and retrieves a characteristic logarithmic aging tendency for 2D-intensity as well as 3D-topographic
images from the sensor. In this paper, we propose to combine such two characteristic aging features with
other 2D and 3D features from the domains of surface measurement, microscopy, photography and spectroscopy, to
achieve an increase in accuracy and reliability of a potential future age determination scheme.
Discussing the feasibility of such variety of sensor devices and possible aging features, we propose a general fusion
approach, which might combine promising features to a joint age determination scheme in future. We furthermore demonstrate
the feasibility of the introduced approach by exemplary fusing the binary pixel features based on 2D-intensity
and 3D-topographic images of the mentioned CWL sensor. We conclude that a formula based age determination approach
requires very precise image data, which cannot be achieved at the moment, whereas a machine learning based
classification approach seems to be feasible, if an adequate amount of features can be provided.