Sex Determination Using Data Mining Methods Through Measurements of Ascender and Descender Parts of Letters
DOI:
https://doi.org/10.17986/blm.1690Keywords:
Gender prediction, forensic handwriting examination, artificial neural networks, data miningAbstract
Objective: Sex determination has been found interesting in forensic handwriting examinations and has been researched by scientists. The inclusion of the sex parameter as a supporting element in the examination of forensic handwriting while deciding belonging will increase the reliability of the results. In this study, it was aimed to investigate the contribution of the ascender and descender parts of the letters to sex prediction by measuring them.
Methods: In line with this purpose, handwriting samples were collected from 50 female and 50 male participants by having them write 11 sentences containing the letters “b, d, f, g, h, k, t, y, p” at initial, medial, and end positions. The ascender and descender parts of these letters were measured in millimeters. Logistics, k-nearest neighbor (KNN), support vector machine (SVM) and artificial neural network (ANN) were selected and applied to these data.
Results: The ascender and descender parts of these letters were measured in millimeters and statistically significant differences were found between male and female participants. The ascender parts of the “b, d, h, k, t” were determined to be statistically significantly longer in males. Accuracy rates are 0.65, 0.60, 0.71 and 0.82 for Logistics, KNN, SVM and ANN, respectively.
Conclusion: In our opinion, this result is promising. If the studies on this subject are increased, higher success rates can be achieved, and more contributions can be made to forensic handwriting examination.
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