Gender Classification Based on Fingerprint Analysis

Abstract

Gender classification plays an active role in several applications such as biometrics, criminology,surveillance, human computer interaction, commercial profiling. Though bbiometric 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 (ReLu) and tanh 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.