Fingerprint Forgery Detection and Person Identification Based on Deep Learning

Abstract

Automated fingerprint recognition and authentication have been widely used in biometrics applications and as a personal identity tool due to their dependability and unique characteristics. The person's fingerprint must be authentic and not altered or forged to be used to verify that person's identity. It is more difficult to determine whether a fingerprint is real /authenticated. The presented work aims to design two models based on a convolutional neural network (CNN) with the ability to detect whether the fingerprints are authenticated or not. The proposed methodology includes two levels: the first involves forgery detection of fingerprints. Whereas the second level exam ines fingerprint identification. Furthermore, a reliable deep learning technique that includes Transfer Learning (TL) and building the architecture of CNN from scratch was utilized to diagnose and identify fingerprints for 100 persons using the SOCO dataset. Thus, the results recorded higher accuracy at 98.69% and 99.08% sensitivity of forgery detection. Furthermore, it achieved an optimal rate for matching fingerprints and outperformed other TL models (VGG16, VGG19, ResNet50) and related works. For this reason, it could be considered a successful model for Biometric fingerprint authentication and forgery detection.