A B-Spline Function Based 3D Point Cloud Flattening Scheme for 3D Fingerprint Recognition and Identification

A B-Spline Function Based 3D Point Cloud Unwrapping Scheme for 3D Fingerprint Recognition and Identification

A survey of human-computer interaction (HCI) & natural habits-based behavioural biometric modalities for user recognition schemes

One of the fields that still today remains largely unexplored in biometrics is 3D fingerprint recognition. This gap is mainly explained by the lack of scanners capable of acquiring on a touchless, fast, reliable and repeatable way, accurate fingerprint 3D spatial models. As such, full 3D fingerprint data with which to produce research and advance this field is almost nonexistent. If such acquisition process was possible, it could represent the beginning of a real paradigm shift in the way fingerprint recognition is performed. The present paper is a first promising step to address the fascinating challenge of 3D fingerprint acquisition and recognition. It presents a new full 3D touchless fingerprint scanner, a new database with 1,000 3D finger-print models, a new segmentation method based on the additional spatial information provided by the models, and initial baseline verification results.

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

A feature-based recognition scheme for fingerprint identification on a massively parallel system is presented. The algorithm provides an orientation-independent recognition system that utilizes the features offered by massively parallel architectures. Implementation of this algorithm on the GAM I pyramid to extract the different features is discussed. The use of an adder pyramid that is incorporated in GAM I architecture for determining the Euler count that is used to recognize the loops in the pattern is also highlighted. The results of identification of simulated patterns on the GAM I pyramid are presented. >

Contactless and partial 3D fingerprint recognition using multi-view deep representation

In recent days , many digital finger print image based identification applications are used widely for human identity . Finger print features like minutiae (ridge lines, bifurcation, ridge ending) delta point, core point, texture patterns of ridges and valleys etc., are used for finger print identification. Reliable finger print identification system uses the finger print features to provide higher accuracy . Many finger print identification systems have been proposed based on minutiae , ridge lines. But these systems performance are not effective as compared to texture based methods. In this article , significance of feature extraction techniques , various texture based finger print identification methods are investigated and analysed . Limitations of minutiae, , and based identification systems are discussed. Also how effective the texture based identification systems are discussed. A comparative analysis of experimental results of various methods of finger print identification is done. This paper is concluded with the finding of appropriate method of texture based feature extraction for effective identity authentication. Keywords : Finger print feature, Texture feature, feature extraction, ridges, finger print recognition.

With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

Traditional fingerprint identification is adopting minutiae point as a template, but this exist template leaked danger. Based on the distribution density of minutiae point, this paper deeply researches on how to use the distribution density of minutiae point as the template of fingerprints, avoiding directly storing minutiae point data, and ensuring the safety of fingerprint template. At the same time, we proposed a fingerprint matching algorithm based on this template. The experimental results show that the matching algorithm is an effective identification scheme.

Radio frequency fingerprint (RFF) identification is an emerging device authentication technique that exploits the hardware imperfections resulting from the manufacturing process. Due to the varying impact of the wireless channel during RFF training and test stages, it is challenging to design channel-independent RFF techniques. This article designs a channel robust RFF identification scheme by leveraging the different spectrum of adjacent signal symbols, named the Difference of the Logarithm of the Spectrum (DoLoS), which does not rely on a single RFF feature or requires additional manipulation of the devices under test. Specifically, DoLoS exploits the fact that two different symbols in a packet exhibit different RFF features but have a similar channel response during the channel coherence time. We implemented the DoLoS with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system as a case study. We carried out extensive experiments using seven Wi-Fi devices of the same model in different wireless channel environments, including 12 data collection positions in two completely different environments. Compared with conventional RFF identification schemes that do not eliminate channel effects, our scheme is robust to channel variations and the highest identification accuracy is 99.02% in the single-environment evaluation and 97.05% in the cross-environment evaluation.

Three-dimensional (3D) imaging provides detailed geometry of real-world objects, unlike 2D image texture. The rudimentary form of 3D imaging is point clouds that are distinctly different from image pixels in terms of structure and processing methods. The 3D computer vision literature primarily retrieves global shape patterns in 3D data for object and face recognition tasks. In contrast, mining local deformation patterns in 3D data that are independent of global shape is a nontrivial task. This paper proposes a computational pipeline for mining baseline local patterns in 3D point clouds and identifies informative segments of point clouds for data selection and interpretation. We investigate the performance of several clustering algorithms in 3D point cloud segmentation and propose a computationally fast multi-stage clustering pipeline with parametric modeling of local patterns in point clouds. The proposed pipeline has achieved an area under the ROC curve of 0.72 in classifying seven emotional expressions (including the neutral expression) using 3D human facial point clouds. Our results demonstrate the baseline efficacy of raw 3D point coordinates in mining local patterns without involving feature engineering or deep learning. Therefore, the proposed pipeline can serve as a baseline for 1) rapid mining of informative local patterns and 2) selecting important segments of 3D point cloud data. The source code is made publicly available to promote future work in this area.

In this study, we develop a system for efficiently measuring detailed information of trees in a forest environment using a small unmanned aerial vehicle (UAV) equipped with light detection and ranging (lidar). The main purpose of forest measurement is to predict the volume of wood for harvesting and delineating forest boundaries by tree location. Herein, we propose a method for extracting the position, number of trees, and vertical height of trees from a set of three-dimensional (3D) point clouds acquired by a UAV lidar system. The point cloud obtained from a UAV is dense in the tree’s crown, and the trunk 3D points are sparse because the crown of the tree obstructs the laser beam. Therefore, it is difficult to extract single-tree information from 3D point clouds because the characteristics of 3D point clouds differ significantly from those of conventional 3D point clouds using ground-based laser scanners. In this study, we segment the forest point cloud into three regions with different densities of point clouds, i.e., canopy, trunk, and ground, and process each region individually to extract the target information. By comparing a ground laser survey and the proposed method in an actual forest environment, it is discovered that the number of trees in an area measuring 100 m × 100 m is 94.6% of the total number of trees. The root mean square error of the tree position is 0.3 m, whereas that of the vertical height is 2.3 m, indicating that single-tree information can be measured with sufficient accuracy for forest management.

The rapid development of communication and computer has brought many application scenarios to the fingerprint identification technology of communication equipment. The technology is of great significance in electronic countermeasures, wireless network security, and other fields and has been widely studied in recent years. The fingerprint identification technology of communication equipment is mainly based on the fingerprint characteristics represented on the transmitted signals of the equipment, which are different from other devices, and the connection between the characteristics and the hardware equipment is established, so as to realize the purpose of identifying the communication equipment. In this paper, the author studies the key technologies related to fingerprint recognition of communication equipment, including signal acquisition, signal feature extraction, and classifier design, and transient signal recognition equipment. In this paper, the integrated learning and deep learning based on fingerprint recognition are taken as the main research contents of communication equipment, and the fingerprint recognition scheme of communication equipment is given; the proposed scheme is verified by the measured data. Aiming at the transient signal of communication equipment, an algorithm using the short-term periodicity of signal is presented. The feature extraction of steady-state signal is realized. The autoencoder feature and four kinds of integral bispectrum feature are analyzed and visualized. Research on communication equipment individual recognition technology is based on ensemble learning. An individual recognition scheme for communication devices based on Extreme Gradient Boosting (XGBoost) classification model is studied. The Gradient Boosting Decision Tree (GBDT) model with different parameters was used as the primary learner of stacking classifier. The steady-state signal recognition of mobile phones based on deep learning is studied. The results show that the stacking recognition rate improved by about 2% compared with GBDT using multiple GBDT models with different parameters as the primary learner.

There are various Iris recognition and identification schemes known to produce exceptional results with very less errors and at times no errors at all but are patented. Many prominent researchers have given their schemes for either recognition of an Iris from an image and then identifying it from a set of available database so as to know who it belongs to. The Gabor filter is a preferred algorithm for feature extraction of Iris image but it has certain limitations, hence Principal Component Analysis (PCA) is used to overcome the limitations of the Gabor filter and provide a solution which achieves better results which are encouraging and provide a better solution to Gabor filters for Off Gaze images.KeywordsGabor FilterPrincipal Component AnalysisIris RecognitionIris IdentificationFalse Acceptance RateFalse Rejection RateEigen ValuesEigen Vectors

Aiming at addressing the defect of the data blindness of a LiDAR point cloud in transparent media such as glass in low illumination environments, a new method is proposed to realize covert target reconnaissance, identification and ranging using the fusion of a shimmering polarized image and a laser LiDAR point cloud, and the corresponding system is constructed. Based on the extraction of pixel coordinates from the 3D LiDAR point cloud, the method adds information on the polarization degree and polarization angle of the micro-light polarization image, as well as on the reflective intensity of each point of the LiDAR. The mapping matrix of the radar point cloud to the pixel coordinates is made to contain depth offset information and show better fitting, thus optimizing the 3D point cloud converted from the micro-light polarization image. On this basis, algorithms such as 3D point cloud fusion and pseudo-color mapping are used to further optimize the matching and fusion procedures for the micro-light polarization image and the radar point cloud, so as to successfully realize the alignment and fusion of the 2D micro-light polarization image and the 3D LiDAR point cloud. The experimental results show that the alignment rate between the 2D micro-light polarization image and the 3D LiDAR point cloud reaches 74.82%, which can effectively detect the target hidden behind the glass under the low illumination condition and fill the blind area of the LiDAR point cloud data acquisition. This study verifies the feasibility and advantages of “polarization + LiDAR” fusion in low-light glass scene reconnaissance, and it provides a new technological means of covert target detection in complex environments.