Fingerprint Based Gender Classification

Abstract

Male-female classification from a fingerprint is an important step in forensic science, anthropological and medical studies to reduce the efforts required for searching a person. The aim of this research is to establish a relationship between gender and the fingerprint using some special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and ridge width. showed that male-female classification can be done correctly up to 88.5% based on white lines count, RTVTR & ridge count using Neural Network as Classifier. We have used RTVTR, ridge width and ridge density for classification and SVM as classifier. We have found male-female can be correctly classified up to 91%. Gender classification plays an active role in several applications such as biometrics, criminology, surveillance, human computer interaction, commercial profiling. Though biometric traits such as face, gait, iris and hand shape are used for gender classification in the past, majority of the work is based on face as it contains more prominent features than others. In this paper we have analyzed fingerprints for gender classification with a hope that it has great potential for future research. We have employed a three convolutional layer CNN with rectified linear and activation functions on NIST database which contains a set of 4000 images and achieved 99% accuracy. Performance of the proposed system demonstrated that fingerprints contains vital features to discriminate gender of a person. Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identify people by measuring some aspect of individual's anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. The year 2015 election in Nigeria was greeted by some petitions including under-aged voters. The need for an age and gender detector system is a major concern for organizations at all levels where integrity of information cannot be compromised. This work developed a system that determines human age-range and gender using fingerprint analysis trained with Back Propagation Neural Network (for gender classification) and DWT+PCA (for age classification). A total of 280 fingerprint samples of people with various age and gender were collected. 140 of these samples were used for training the system"s Database; 70 males and 70 females respectively. This was done for age groups 1-10, 11-20, 21-30, 31-40, 41-50, 51-60 and 61-70 accordingly. In order to determine the gender of an individual, the Ridge Thickness Valley Thickness Ratio (RTVTR) of the person was put into consideration. Result showed 80.00 % classification accuracy for females and 72.86 % for males while 115 subjects out of 140 (82.14%) were correctly classified in age.