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The identification of mammalian species through the classification of hair patterns using image pattern recognition

The identification of mammals using hair is important in the fields of forensics and ecology. The application of automated pattern recognition techniques to this process provides a means of reducing the subjectivity found in the process, as manual techniques rely on the interpretation of a human expert rather than quantitative measures. The first application of image pattern recognition techniques to the classification of African mammalian species using hair patterns is presented. We design a classification sub-process for each of the two hair characteristics used to classify hair; scale and cross section patterns. The scale pattern sub-process utilises a 2D Gabor filter-bank to extract features from a scale pattern image and these features are classified using a minimum distance measure. The cross section sub-process utilises Hu’s moments to extract features and a minimum distance measure to classify these features. Implementation of the scale pattern sub-process produces a best result of 64% accuracy when using a filter-bank of size eight and a best result of 76% when using a filter-bank of size sixteen. The implementation of the cross-section sub-process produces a best result of 48%. These results indicate that hair scale patterns are more diagnostic than hair cross section patterns in determining the identity of a hair through the use of automated pattern recognition techniques.