Similarity analysis of actual fake fingerprints and generated fake fingerprints by DCGAN

Abstract

This paper proposes a verification method whether fake fingerprints generated by DCGAN are similar to actual fake fingerprints in order to augment fake fingerprint data. The first method to verify is to compare the distributions of the mean and standard deviation of fake fingerprints generated by deep convolutional generative adversarial network (DCGAN) with those of actual fake fingerprints. In the second method, the mean Hamming distance, which is a method of evaluating the similarity of images, is used for measuring the similarity between the generated fake fingerprints and the actual fake fingerprints. The third method is to obtain the histograms of the generated fake fingerprints and actual fake fingerprints and measure the similarity by calculating Pearson correlation of the histograms. The fourth method is to calculate intersection of union, which is a method of evaluating the shape similarity of images, between generated fake fingerprints and actual fake fingerprints. From extensive experiments it was confirmed that fake fingerprints generated by DCGAN could be used to augment fake fingerprint data because generated fake fingerprints are similar to actual fake fingerprints in terms of four similarity measures.