Latent Fingerprint Identification Using Deep Learning Method

Abstract

Digital fingerprint is one of the most consistent modalities in up to date biometrics and hence has been broadly studied and deploy in real applications. The accuracy of one Automatic Fingerprint Identification System (AFIS) largely depends on the quality of fingerprint samples, as it has an important impact on the degradation of the matching (comparison) error rates. This thesis generally focuses on the evaluation of biometric quality metrics and Fingerprint Quality Assessment (FQA), particularly in estimating the quality of gray-level latent fingerprint images or represented by minutiae set. By making a refined review of both biometric systems and relevant evaluation techniques, this contribute by the definition of a new evaluation or validation outline for estimating the performance of biometric quality metrics. It is defined to check the quality of latent fingerprint images by statistically measured parameters. In this work, an automatic Region-Of-Interest (ROI)-based latent fingerprint quality assessment technique is proposed by using deep learning. The first stage in our model uses deep learning, namely Region Convolutional Neural Network (R-CNN) to segment a latent fingerprint. In the second stage, feature vectors computed from the segmented latent fingerprint are used as input to a multi-class perceptron that predicts the value of the fingerprint. This proposed approach eliminates the need for manual ROI and feature markup by dormant examiners. Finally, experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction