Gender inference within Turkish population by using only fingerprint feature vectors

In the literature, there are some studies which investigate if there is a relationship between fingerprint and gender or not. In these studies, this relationship is examined based on some vectorial parts of fingerprints. The main problem in these studies is the lack of data, depending on ethnical background and country, and there is not an exact finding of true classification results. It is known that fingerprints show difference in males and females, and it is explained that women's line details are thin whereas men's line details are thick. However, the statistical studies, which have been made to prove the relationship between fingerprint and gender, have not investigated if the hypothesis is true for all ethnical backgrounds. In this study, we have examined if gender inference can be made only through fingerprint feature vectors, which belong to Turkish subjects, by using our database consisting of Naive Bayes, kNN, Decision Tree and Support Vector Machine learning algorithms. By using Naive Bayes algorithm, the success of the gender classification is found as 95.3%. This ratio has not been obtained before for “gender inference from fingerprint” in the literature. Therefore, this study can be useful for criminal cases.