Abstract
Chemical imaging is a new latent fingerprint examination
technique that combines molecular spectroscopy and digital
imaging technology. Chemical imaging, employing luminescence and
visible absorbance, has been successfully applied to various
treated and untreated fingerprint samples, demonstrating the
usefulness of this technology to aid routine forensic latent
fingerprint development. This validated technique exhibits
improved detection limits over conventional approaches. Chemical
imaging has also been used to demonstrate increased contrast of
fingerprints developed on difficult backgrounds such as
fluorescent, dark, and rough substrates and multicolored
surfaces. Chemical imaging is a viable strategy for detecting
the most challenging latent fingerprints when standard
development methods fail.
Introduction
Chemical imaging combines molecular,
spectroscopic, and digital imaging information by recording
images of the sample as a function of wavelength through the use
of an efficient electro-optic imaging spectrometer. The
electro-optic imaging spectrometer combines an efficient
electronic filter system with no moveable parts and a slow-scan
CCD detector. Samples are illuminated using a standard variable
wavelength light excitation source (SPEX Forensics, Edison, New
Jersey) followed by image data collection through the imaging
spectrometer at preselected wavelength increments. The resulting
images are combined to create a multidimensional image data set.
A fully resolved spectrum is recorded for each pixel location in
the image where each spectrum can provide information to better
describe the sample of interest (Morris et al. 1994; Morris et
al. 1996). Contrast in the resulting chemical images arises from
the varying amounts of absorption, emission, or scatter that
occur in the measured spectrum at each image pixel. As a result,
chemical images provide molecular, compositional, structural, or
quantitative information about the sample of interest in
addition to generating enhanced image contrast.
Conventional fingerprint imaging systems use standard variable
wavelength light excitation followed by data collection at one
specific color, often employing a single-barrier optical filter
configuration. As a result, fingerprint detection on complex
substances, including paper, curved surfaces, or dark objects
can be challenging. Chemical imaging separates an image into its
component colors in a quantitative manner at many different
wavelengths. Many more resulting wavelengths are recorded than
conventional red, green, and blue color imaging. Typically,
hundreds to thousands of colors can be accessed using a novel
electronic imaging spectrometer. This data enables the examiner
to discern usable information from a background on a
pixel-by-pixel basis. Unwanted background including
fluorescence, texture, and colors can be efficiently minimized,
effectively revealing the detail of the fingerprint pattern. The
enhanced sensitivity of chemical imaging has been demonstrated
for fingerprint examination that exceeds the capabilities of
conventional imaging techniques (Exline et al. in press; Wallace
2001).
To collect chemical images, the imaging spectrometer is
computer-controlled; hence, the parameters for a particular
series of experiments need only be set up once and can be
automated, which is a distinct advantage. Also, conventional
luminescence imaging systems require the use of suitable barrier
filters that block the reflected excitation light and only
transmit the weak fingerprint emission. With chemical imaging,
the imaging spectrometer acts as the barrier filter that
eliminates the need for additional filter optics. Another
advantage of chemical imaging is that, with limited knowledge of
a particular fingerprint’s absorption or luminescence and
despite the presence of background interference from the
substrate, the system can be configured to analyze the
fingerprint emission over a wide spectral range. Software that
locates and isolates the maximum absorbance or emission of a
treated fingerprint, thereby optimizing image contrast can then
be used. Increased contrast in the imaging data is also enhanced
by reducing background signal and revealing the fingerprint
signal through the use of robust, well-tested, and validated
multivariate statistical analysis tools.
Methodology and Results
The CONDOR™ Macroscopic Chemical Imaging System (ChemImage
Corporation, Pittsburgh, Pennsylvania) was equipped with a
visible wavelength range electro-optic imaging spectrometer and
a front illuminated 1024x1024 slow-scan CCD detector on a
macroscopic imaging platform. The system provides 1,048,576
spatially resolved spectra for each data set collected. The 16:1
visible macro optics enabled images to be collected at fields of
view ranging from 1.68 to 108mm with 25-400µm spatial
resolutions, respectively, in a 16-bit digital image format. The
excitation source was a halide arc lamp used in combination with
a range of excitation filters. A major difference between
chemical imaging and conventional methods of latent fingerprint
detection is the use of a liquid crystal-based electro-optic
imaging spectrometer. Wavelength-resolved images could be
collected from 400 to 720nm at sub-nm tuning increments, which
provides flexible sampling of the optical wavelengths for
generating fingerprint contrast. Typical operation of the system
does not require sampling at sub-nm increments.
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Figure 1. |
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Figure 1A.
Untreated latent fingerprint on a paper surface using
conventional 35mm photography. |
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Figure 1B.
The same fingerprint developed using visible absorption
chemical imaging followed by substrate division to correct
for background effects. |
Figure 1
shows reflectance chemical images of an untreated latent
fingerprint on white paper captured over 420 to 720nm at 10nm
increments. Figure 1A shows a color image of the latent
fingerprint. Figure 1B shows a visible reflectance chemical
image of the fingerprint. To produce Figure 1B, ChemAcquire™ 6.0
software (ChemImage Corporation, Pittsburgh, Pennsylvania) was
used to collect the chemical image and a background image in a
clear region of the paper using identical collection parameters.
The original chemical image was divided by the background
chemical image to correct for several background effects,
including illumination light source variation, substrate
reflectance, and instrument response.
Chemical image analysis is provided by ChemAnalyze™ 6.0 software
(ChemImage Corporation, Pittsburgh, Pennsylvania). Contrast is
generated in the images based on the relative amounts of light
that are produced by the different species located throughout
the sample. Since a spectrum is generated for each pixel
location, chemometric analysis tools such as principal component
analysis (Wold et al. 1987) and multivariate curve resolution
(Andrew and Hancewicz 1998) can be applied to the image data to
extract pertinent information otherwise missed by ordinary
univariate (single wavelength) measures.
Figure 2 shows analysis of a fingerprint treated with physical
developer on counterfeit U.S. currency using luminescence
chemical imaging. An excitation filter at 575nm was employed
while tuning the imaging spectrometer from 580 to 720nm at 5nm
increments. The analysis involved dividing the latent
fingerprint chemical images by a background image to ratio out
the substrate emission. This procedure was accomplished by
selecting an average of pixels in a region between the existing
ridge patterns defining this averaged spectrum as background and
dividing all pixels in the image by the background spectrum.
Subsequently, each pixel in the resulting chemical image was
subtracted by a global minimum value to reduce offset, and then
a vector normalization procedure was performed. Vector
normalization involves dividing each pixel spectrum by the
square root of the sum of the squares of all the pixel spectra,
which has the effect of bringing intense image features on
approximately the same scale as weak image features. Principal
component analysis was then applied to the normalized data to
produce fingerprint images for the light background (Figure 2B)
and the dark background (Figure 2C). Figure 2D is produced by
averaging the principal component analysis extract images in
ChemAnalyze™ 6.0.
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Figure 3. |
Figure 3A.
Optical image of a blue drug bag. |
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Figure 3B.
Visible-absorption chemical imaging of a ninhydrin-treated
fingerprint following multivariate statistical analysis. |
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Figure 3C.
Digital image of a ninhydrin-treated fingerprint following
conventional digital photography and image processing.
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Images
are reprinted with permission of the Allegheny County
Coroner’s Office,
Forensic Laboratory Division, Pittsburgh, Pennsylvania. |
Figure 3 shows a latent fingerprint present on a drug bag
(Figure 3A) treated with ninhydrin. The fingerprint was examined
and chemically imaged from the 420 to 720nm range in 10nm
increments using white light excitation. Background correction,
offset correction, and normalization procedures comparable to
those applied in Figure 2 were employed to produce Figure 3.
Principal component analysis was then applied to the normalized
data for visualization of the fingerprint (Figure 3B). The same
ninhydrin-treated fingerprint was photographed using a digital
camera and processed using More Hits™ (PC Professionals, Inc.,
Lakewood, Washington) image-enhancement software (Figure 3C).
Why is Chemical Imaging Important?
The successful analysis of untreated latent
fingerprints on paper surfaces by chemical imaging shows immense
promise, given that ridge detail can be detected on fresh
fingerprints using chemical imaging followed by ChemAnalyze™
software analysis. This is an area for continued studies,
because a nondestructive optical method for detecting untreated
prints would be of significant benefit. Results presented here
demonstrate the enhanced sensitivity of chemical imaging for
latent fingerprint examination that exceeds the capabilities of
conventional imaging techniques.
Visualization of the fingerprint on the counterfeit $10 bill
demonstrates the ability of chemical imaging to generate
contrast in the presence of complex substrates by using the
optical properties of the substrate as a digital signature of
the background. Whereas the fingerprint was nonluminescent,
dividing the background spectrum from each pixel in the chemical
image and applying robust chemometric routines could still
develop fingerprint contrast. As a result, the visualization of
the fingerprint was acquired in seconds, where previous efforts
using conventional means have been unsuccessful. Once chemical
imaging revealed the optimal detection strategy, examiners were
able to replicate this study using a VSC 2000™ system (Foster
and Freeman, Worcestershire, United Kingdom).
The ninhydrin-treated fingerprint was developed using chemical
imaging and also by conventional digital photography. The
chemical image result was significantly better as demonstrated
by enhanced ridge detail when compared to the results produced
by conventional methods. This comparison exemplifies the value
of chemical imaging to real-world case samples and its value as
an enhanced detection strategy.
Conclusions
In recent years, new technology has been sought
for more rapid examination of forensic evidence, as well as for
field use. This includes the need for improved fingerprint
visualization methods. A new class of technology based on
chemical (i.e., spectroscopic) imaging has demonstrated the
ability to provide substantial improvements in detection
capability. Chemical imaging techniques integrate digital
imaging with a number of proven optical inspection analytical
methods, including fluorescence imaging spectroscopy and
visible/NIR reflectance spectroscopy. Chemical imaging is
consistently being proven to be a valuable tool for forensic
science, by enabling examiners to visualize and identify
evidence with improved detection limits.
This overview has demonstrated the application of chemical
imaging for the detection of untreated and chemically treated
latent fingerprints. Current studies are ongoing to further
validate and optimize the technique for a wider range of
fingerprint detection methods and for latent fingerprints on a
wider range of substrates.
Acknowledgments
The
authors would like to thank Christie Wallace and Claude Roux
from the University of Technology, Sydney, Australia, and Chris
Lennard from the Australian Federal Police, Canberra, Australia,
for their valuable contributions in implementing this
technology. We would also like to thank Dr. Antonio A. Cantu
from the U.S. Secret Service, Washington, DC, for his insights
into the applications of this technology. Lastly, we would like
to thank Wayne Reutzel and Mike Fedor from the Allegheny County
Crime Laboratory, Pittsburgh, Pennsylvania, for their support
and assistance in introducing this technology to practical
casework methodologies.
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