3D Fingerprint Gender Classification Using Deep Learning

Abstract

Optical Coherence Tomography (OCT) is a high resolution imaging technology, which provides a 3D representation of the fingertip skin. This paper for the first time investigates gender classification using those 3D fingerprints. Different with current fingerprint gender classification methods, the raw multiple longitudinal(X-Z) fingertip images of one finger can be applied instead of studying features extracted from fingerprints, and the model can be trained effectively when the training data set is relatively small. Experimental results show that the best accuracy of 80.7% is achieved by classifying left fore finger on a small database with 59 persons. Meanwhile, with the same data size and method, the accuracy of classification based on 3D fingerprints is much higher than that based on 2D fingerprints: the highest accuracy is increased by 46.8%, and the average accuracy is increased by 26.5%.