FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement (original) (raw)
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The restrictions posed by the recent trans-border regulations to the usage of biometric data force researchers in the fields of digitized forensics and biometrics to use synthetic data for development and evaluation of new algorithms. For digitized forensics, we introduce a technique for conversion of privacy-sensitive datasets of real latent fingerprints to "privacy-friendly" datasets of synthesized fingerprints. Privacy-friendly means in our context that the generated fingerprint images cannot be linked to a particular person who provided fingerprints to the original dataset. In contrast to the standard fingerprint generation approach that makes use of mathematical modeling for drawing ridge-line patterns, we propose applying a data-driven approach making use of generative adversarial neural networks (GAN). In our synthesis experiments the performance of three established GAN architectures is examined. The NIST Special Database 27 is exemplary used as a data source of re...
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Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a metho...
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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