A machine learning approach for fingerprint based gender identification

This paper deals with the problem of gender classification using fingerprint images. Our attempt to gender identification follows the use of machine learning to determine the differences between fingerprint images. Each image in the database was represented by a feature vector consisting of ridge thickness to valley thickness ratio (RTVTR) and the ridge density values. By using a support vector machine trained on a set of 150 male and 125 female images, we obtain a robust classifying function for male and female feature vector patterns.