Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018

An FPGA-based Embedded System For Fingerprint Matching Using Phase Only Correlation Algorithm

none5There is an increasing interest in inexpensive and reliable personal identification in many emerging civilian, commercial and financial applications. Traditional systems such as passwords, PINs, Badges, Smart Cards and Tokens may either be stolen or easy to guess but also to forget, in same cases they may be lost by the user who carries them; all this can lead to identified. Fingerprint-based identification is one of the most used biometric techniques in automated systems for personal identification and it is becoming socially acceptable and cost-effective, since a fingerprint is univocally related to a particular individual. Typical fingerprint identification methods employ feature-based image matching, where minutiae points in the ridge lines (i.e., ridge endings and bifurcations) are identified. Unfortunately this approach is highly influenced by fingertip surface condition. Fingerprint recognition is a complex pattern recognition problem. The efforts to make automatic the matching process based on digital representation of fingerprints, led to the development of Automatic Fingerprint Identification Systems (AFIS). Typically, there are millions of fingerprint records in a database which needs to be entirely searched for a match, to establish the identity of the individual. In order to provide a reasonable response time for each query, it will be better to develop special hardware solutions to implement matching and/or classification algorithms in a really efficient way. In this work we realised a system able to outperform modern PCs in recognising and classifying fingerprints and based on FPGA technology.Il lavoro si è classificato al II posto nell'Altera Contest 2009 Innovate Italy, gara annuale indetta da Altera tra progetti di team di giovani studenti universitari su tutto il territorio nazionale.Giovanni Danese; Mauro Giachero; Francesco Leporati; Giulia Matrone; Nelson NazzicariDanese, Giovanni; Giachero, Mauro; Leporati, Francesco; Matrone, Giulia; Nelson, Nazzicar

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research