The advent of nuclear DNA (nuDNA) analysis altered the way forensic biology
was both practised and viewed by the forensic biologists, police, the legal system
and the general public. The ability of nuDNA to individualise analysis of evidence
and attach a statistical frequency ratio to the result, created an expectation that
numerical objectivity should be part of all forensic analysis. There are few
scientists who would disagree with both the need and desirability of objective
measures of their results. Forensic hair examiners are no exception as indicated by
numerous scientific publications specifically discussing means of objectively
assessing hair and its characteristics. While mitochondrial DNA offers a partially
objective measure of hair the result is destructive of the sample. A method that
objectively supports the hair analysts' microscopic findings and is non destructive
would be beneficial to forensic hair examination. This project attempted to develop
an objective measure of hair analysis by using both traditional light microscopic
comparative techniques combined with a high end digital imaging and image
analysis capacity.
Where objectivity equals an empirical set of numbers that can be manipulated for
statistical significance, the comparative biological sciences such as histology,
anthropology and forensic hair examination struggle. Forensic hair examiners have
long acknowledged the difficulty, even inability, of assigning numerical values to
the features that characterise one hair as being different from another. The human
scalp hair is a "morphological" unit that is not readily split into component parts or
even that these parts lend themselves to a number value. There have been at least
nine separate studies which favourably compare the specificity of microscopic hair
examinations. The challenge this study addressed was to appraise the use of
numerical features in forensic hair examination, with particular emphasis on those
features currently resisting numerical evaluation; specifically, colour and
pigmentary characteristics.
The techniques used were based on obtaining high quality digital images, and using
the pixels inherent in the images to obtain numerical values of such features as
colour and pigmentation. The project sample was taken from the telogen scalp hairs obtained from the hairbrushes of ten nominally brown haired Caucasians, both
male and female. The focus was twofold:
o Compare colour analysis of hair images from brown haired Caucasians
within three standard, internationally recognized colour models, namely
Red-Green-Blue (RGB) colour model; CIE XYZ Tristimulus (1931) colour
model; and CIE L*a*b* (1976) colour model.
o Using the same sets of digital images, undertake pattern recognition
analysis both intra and inter individual hair samples.
Discriminate analysis of the mean colour values collected for each of the inherent
colour variables in the three colour models (red, green, blue; X, Y, Z and L*, a*,
b*) indicated the RGB colour model gave the least separation of brown haired
individuals; CIE XYZ and CIE L*a*b* separated several individuals for all their
individual samples and several other individuals were mostly separated with only
one of their own samples overlapping with another.
Pattern analysis used a small area that represented the overall pigment patterning
observed along the length of the hair shaft. This area was extracted from the digital
image within V++ Digital Optics image analysis software. The extracted pattern
piece was then compared with other sample images within the same hair and four
other hairs from the same individual. Pattern extracts were also compared between
person hair samples. The comparisons generated a set of numerical values based
on the pixel number on the "x" axis of the whole image and the average difference
between the extracted pattern image and the whole image. Analysis of this data
resulted in log distributions when persons were matched with themselves. It was
also possible to refer an unknown pattern extract to this distribution and based on
probabilities, predict as to whether or not the unknown sample fell within any of
the known sample's distribution.
| Identifer | oai:union.ndltd.org:ADTP/219594 |
| Date | January 2007 |
| Creators | Brooks, Elizabeth M, na |
| Publisher | University of Canberra. School of Health Sciences |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright Elizabeth M Brooks |
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