Fingerprint Identification - New Directions

In most of the algorithms that have been suggested in this report, the fingerprint image is reduced to a relatively short sequence of integers. This reduces the memory size required by the database. Each algorithm is intended to exploit specific properties and features of the fingerprint that vary from finger to finger, and that can be localized relatively fast using digital techniques, thus also reducing the computational time requirements to a minimum. In each case, the sensitivity of the algorithm to small variations in the image was also discussed, with the aim of reducing the False Rejection Rate, and of increasing the general robustness of the algorithm

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

A New Technique to Fingerprint Recognition Based on Partial Window

Fingerprint verification is a well-researched problem, and automatic fingerprint verification techniques have been successfully adapted to both civilian and forensic applications for many years. This paper present a new technique to fingerprint recognition based a window that contain  core point this window will be input ANN system to be model we can recognize another fingerprint , so we will firstly,  A recognition algorithm needs to recover fingerprints pose transformation between the input reduce time computation. Our detection algorithm works in the field orientation of the adaptive smoothed with a varying area. The adaptive window is used to attenuate the noise effectively orientation field while maintaining the information of the detailed guidance in the area of ??high curvature. A new approach to the core point location that is proposed is based on hierarchical analysis orientation consistency. The proposed adaptation singular point detection method increases the accuracy of the algorithm. Experiments show that our algorithm developed consistently locates a reference point with high precision only for all fingerprints. And very faster for recognition process. Keywords: Fingerprint recognition; field orientation; neural networks; core point, neural networks

Scale Invariant Feature Transform Based Fingerprint Corepoint Detection

The detection of singular points (core and delta) accurately and reliably is very important for classification and matching of fingerprints. This paper presents a new approach for core point detection based on scale invariant feature transform (SIFT). Firstly, SIFT points are extracted ,then reliability and ridge frequency criteria are applied to reduce the candidate points required to make a decision on the core point. Finally a suitable mask is applied to detect an accurate core point. Experiments on FVC2002 and FVC2004 databases show that our approach locates a unique reference point with high accuracy. Results of our approach are compared with those of the existing methods in terms of accuracy of core point detection.Defence Science Journal, 2013, 63(4), pp.402-407, DOI:http://dx.doi.org/10.14429/dsj.63.270

Wireless and Physical Security via Embedded Sensor Networks

Wireless Intrusion Detection Systems (WIDS) monitor 802.11 wireless frames (Layer-2) in an attempt to detect misuse. What distinguishes a WIDS from a traditional Network IDS is the ability to utilize the broadcast nature of the medium to reconstruct the physical location of the offending party, as opposed to its possibly spoofed (MAC addresses) identity in cyber space. Traditional Wireless Network Security Systems are still heavily anchored in the digital plane of "cyber space" and hence cannot be used reliably or effectively to derive the physical identity of an intruder in order to prevent further malicious wireless broadcasts, for example by escorting an intruder off the premises based on physical evidence. In this paper, we argue that Embedded Sensor Networks could be used effectively to bridge the gap between digital and physical security planes, and thus could be leveraged to provide reciprocal benefit to surveillance and security tasks on both planes. Toward that end, we present our recent experience integrating wireless networking security services into the SNBENCH (Sensor Network workBench). The SNBENCH provides an extensible framework that enables the rapid development and automated deployment of Sensor Network applications on a shared, embedded sensing and actuation infrastructure. The SNBENCH's extensible architecture allows an engineer to quickly integrate new sensing and response capabilities into the SNBENCH framework, while high-level languages and compilers allow novice SN programmers to compose SN service logic, unaware of the lower-level implementation details of tools on which their services rely. In this paper we convey the simplicity of the service composition through concrete examples that illustrate the power and potential of Wireless Security Services that span both the physical and digital plane.National Science Foundation (CISE/CSR 0720604, ENG/EFRI 0735974, CIES/CNS 0520166, CNS/ITR 0205294, CISE/ERA RI 0202067

Analysis of extracted fingerprint texture features

This paper describes an approach for electronic feature extraction from fingerprint images for identification purposes. Statistical analysis of the features extracted from the fingerprint provides the advantage of real-time analysis of the fingerprint information, providing a method to remove unwanted information prior to the matching stage required in electronic fingerprint verification. This method can also detect of noise within the fingerprint image and location of critical data in the fingerprint. In order to demonstrate the effectiveness of this work, experiments have been developed and conducted with the results and analysis provided at the end of this paper for discussion.<br /

Poor Quality Fingerprint Recognition Based on Wave Atom Transform

Fingerprint is considered the most practical biometrics due to some specific features which make them widely accepted. Reliable feature extraction from poor quality fingerprint images is still the most challenging problem in fingerprint recognition system. Extracting features from poor fingerprint images is not an easy task. Recently, Multi-resolution transforms techniques have been widely used as a feature extractor in the field of biometric recognition. In this paper we develop a complete and an efficient fingerprint recognition system that can deal with poor quality fingerprint images. Identification of poor quality fingerprint images needs reliable preprocessing stage, in which an image alignment, segmentation, and enhancement processes are performed. We improve a popular enhancement technique by replacing the segmentation algorithm with another new one. We use Waveatom transforms in extracting distinctive features from the enhanced fingerprint images. The selected features are matched throw K-Nearest neighbor classifier techniques. We test our methodology in 114 subjects selected from a very challenges database; CASIA; and we achieve a high recognition rate of about 99.5%