IIAE CONFERENCE SYSTEM, The 5th IIAE International Conference on Industrial Application Engineering 2017 (ICIAE2017)

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Classifying Human and Animal Hair Using Probabilistic Neural Networks for Texture Classification
Jem Mae Tan Donato, Sandra Mae Famador

Last modified: 2017-02-21

Abstract


Hair Analysis has been widely used in the field of forensics especially in finding out if a piece of hair evidence in a crime scene is considered valid. This study proposes a classification method for human head hair and animal hair using Probabilistic Neural Network. Hair samples were mounted in a compound microscope, digitized using a camera and stitched using method used by Rosebrock. Images were magnified 100 times and 400 times. Sobel edge detection was used for texture analysis. Gray-level cooccurrence matrix was applied to all samples to extract features. Results were then fed to a Probabilistic Neural Network for classification. The datasets were validated using the 2-fold cross validation, wherein 2 training sets and 2 test sets were made.  An 84.615 percent accuracy was achieved for normalized data and a 100 percent accuracy was achieved for unnormalized data.

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