Premium
Analysis of hair color and texture for forensic examinations
Author(s) -
Funes David S. H.,
Bridge Candice
Publication year - 2021
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.14640
Subject(s) - hue , rgb color model , artificial intelligence , texture (cosmology) , pattern recognition (psychology) , computer vision , computer science , mathematics , image (mathematics)
Efforts have been conducted to evaluate hair features empirically, for example, color; however, a review of current literature showed few studies investigating cortical texture analysis. The development of high‐resolution digital microscopes allows researchers to obtain more accurate measurements of hair features. In this study, digital microscopy was used to explore variance within the cortical texture, color, and density characteristics throughout hair strands. In this study, 20–25 naturally shed hairs from 12 individuals of different ancestries were collected. Measurements of three different features were collected: entropy texture measurements, that is, measurement of the randomness of pigment granules and cortical fusi; color distributions of the hairs via a red–green–blue (RGB) color model; and the calculation of the pigment density ratio using hue–saturation–value color model. Analysis of variance was performed on data collected from each analysis type to assess inter‐ and intra‐person variability. The F ‐ratios obtained, which compares inter‐person to intra‐person variability, ranged from 9.29 to 69.24. Cortical texture and color measurements showed promising results in differentiating between inter‐person samples. Although density ratios showed the least potential for discrimination, it provided another level for differentiating inter‐person hair samples. The location that provided the best differentiation of strands from different donors could be made at a 20.0 mm distance from the strand's proximal end for all three features measured. The methods proposed in this study show the potential to quantify hair features that exhibit better differentiation of hair from different individuals.