Exline - Forensic Science Communications - July 2003
July 2003 - Volume 5 - Number 3
Research and Technology
David L. Exline
Rebecca L. Schuler
Patrick J. Treado
Chief Technology Officer
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.
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.
Untreated latent fingerprint on a paper surface using conventional 35mm photography.
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.
Visible-absorption chemical imaging of a ninhydrin-treated fingerprint following multivariate statistical analysis.
Digital image of a ninhydrin-treated fingerprint following conventional digital photography and image processing.
|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.
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.
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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