Adaptive fingerprint pore modeling and extraction

High-resolution automated fingerprint recognition systems (AFRSs) offer higher security because they are able to make use of level-3 features, such as pores, that are not available in lower resolution ( <; 500-dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and performance of an AFRS is the choice of image resolution. In this paper, we identify the optimal resolution for an AFRS using the two most representative fingerprint features: minutiae and pores. We first designed a multiresolution fingerprint acquisition device to collect fingerprint images at multiple resolutions and captured fingerprints at various resolutions but at a fixed image size. We then carried out a theoretical analysis to identify the minimum required resolution for fingerprint recognition using minutiae and pores. After experiments on our collected fingerprint images and applying three requirements for the proportions of minutiae and pores that must be retained in a fingerprint image, we recommend a reference resolution of 800 dpi. Subsequent tests have further confirmed the proposed reference resolution.

The fluid mobility characteristics in the pores with various apertures for tight-sandstone would finally determine the fluid mobility and production of tight oil and gas reservoirs. In this study, the tight-sandstone core samples collected from Middle Jurassic Xishanyao Formation in Santanghu Basin were launched the fluid mobility measurements under various centrifugal speeds. With the NMR fractal theory pore classification method, the various types of pores in the tight-sandstone core samples were classified, and the fluid mobility in different types of pores were also investigated. The results show that the tights-sandstone core samples were significantly influenced by compaction, and the core samples are relatively dense, the mineral intergranular solution pores and colloidal intergranular pores are the main storage spaces. With a constant increase of the centrifugal speed, the fluid mobility increases continuously, and the fluid mobility for CTOS-19 features stronger than that of CTOS-7, which is related to the complexity of pore structure in tight-sandstone sample. Compared with the pore aperture in CTOS-19, the pore aperture in CTOS-7 is smaller, and the connectivity between the smaller and larger pores is poorer, leading to the poorer fluid mobility. Besides, the NMR fractal theory pore classification method also shows that the COTS-7 features more pore types than COTS-19, five and four types respectively. The type P2 and P3 pores are dominant in COTS-7 and CTOS-19 core samples, and the connectivity between type P2 and P3 pores contributes dominantly to the fluid mobility. With the NMR fractal theory pore classification method, the complexity of the distribution of fluid and fluid mobility in tight reservoirs could be studied quantitatively, and the results can efficiently guide the development of residual oil and gas in tight oil reservoirs.

Reference point identification is important in automatic fingerprint recognition system as it can be used to align fingerprints in a correct orientation in spite of the possibility of different transformations in fingerprint images. It is also used in fingerprint classification, as it is desirable to classify fingerprint images for forensic type applications which require the input image to be verified against a large database. The important feature information useful for classification is centered near the reference point. Most of the current approaches for identifying the reference point either require determining ridge orientation or use some complex filters. These methods either operate on 2D (two dimensional) or are not robust to rotation or cannot be applied to every class of fingerprint image. This paper proposes a method to reliably identify unique reference point that operates in 1D (one dimensional). The method treats the fingerprint ridges as a non-overlapped sequence of chain code segments. A modified k-curvature method has been proposed to find the high-curvature area of fingerprint ridges. The reference point localization is based on the property of the ridge's bending energy. The proposed method is tested on FVC2002 and FVC2004 standard datasets, and the experimental results show that the proposed algorithm can accurately locate reference point for all types of fingerprint images.

Lack of robust segmentation against degraded quality image is one of the open issues in fingerprint segmentation. Good fingerprint segmentation effectively reduces the processing time in automatic fingerprint recognition systems. Poor segmentation result in spurious and missing features thus degrading performance of overall system. Segmentation will be more effective if done in accordance to the quality of image. Fingerprint images with high quality have wide range of features which can be used for segmentation than the low quality image, where the fingerprint features are not clearly visible. This paper focus on the two folded segmentation process comprising of quality evaluation and segmentation based on it. Various global and local features are used for assessing quality of image and thereby using them for segmenting ridge area from plain background. The segmented images are compared using percentage of foreground area to total area, genuine number of minutiae points extracted from segmented area. The time taken for image segmentation is also used as a performance parameter. The proposed approach has been tested with images of different qualities from NIST and FVC data sets and the results are proven to be better than the conventional segmentation approaches.

BackgroundOrientation field (OF) plays a very significant role in automatic fingerprint recognition systems. Many algorithms have been proposed for the estimation of fingerprints’ OF but it is hard to solve the dilemma of correcting spurious ridge structure and avoiding singularity location deviation, especially for poor images. So far, the following drawbacks still need to be solved for OF construction methods for practical application: (1) How to adaptively choose block scales to resolve the contradiction between accuracy and anti-noise, since small scale is beneficial to accuracy but is sensitive to noise, while large scale is more resistant to noise, but the accuracy is deteriorated. (2) How to construct the genuine OF in the areas close-by singular points and to evade singularity location deviation? Current block based methods give spurious OF estimates in the area near singular points because these areas have large curvature thus the detected singular points deviate from the genuine localizations. When these singular points are used as the anchor for referencing minutiae, it makes the average error of matching or recognition even larger. Therefore, it is essentials to construct the genuine OF in the areas close-by singular points and to evade singularity deviation.MethodsTo overcome the above-mentioned limitations, a novel method, combining a weighted multi-scale composite window (WMCM) with a hierarchical smoothing strategy has been proposed for the computation of fingerprint OF. This method mainly contains two procedures: the approximate OF estimation and the hierarchical OF smoothing. In the first procedure, a series of OFs are established under multiple scales of composite windows by using a gradient based method then a coarse OF is estimated using the weight of each scale determined by a squared gradient consistency. In the second procedure, the OF is first quantized into a two-digitized orientation zone and a two-orientation-zone filtering strategy is adapted to the OF blocks based on a filtering mask obtained after eliminating the isolated blocks. In the end a similar three-digitized orientation zone is performed to obtain an accurate and smooth OF. To validate the performance, the proposed method has been applied to OF computation using the FVC2004 databases and three experiments are designed. Experiment 1 aims to validate whether the weighted multi-scale composite window can balance the dilemma of accuracy and robustness more effectively than the previous works do. Experiment 2 is designed to examine whether the hierarchical smoothing method can correct the spurious ridge flow and preserve the genuine localization of singular points. The purpose of experiment 3 is to test the performance of the proposed method on OF reconstruction in low quality fingerprint images. The fingerprint databases FVC 2004 DB1–DB4 are employed in this study.ResultsThe results of experiment I shows that the proposed method is capable to extract the information of OF reliably and it is more robust against singularity localization deviation in comparison with the other three gradient based methods. The results of experiment II indicates that the proposed smoothing method can balance the contradiction in correcting spurious ridge structures and preserving genuine singularity localization. The results of experiment III illustrates that our approach combing WMCW with the hierarchical smoothing method is capable to extract the information of OF ridge reliably and it is more robust against singularity deviation in comparison with the other three gradient based methods. In a word, the experiment results demonstrate that the proposed method can correct spurious ridge structure and meanwhile avoid singularity deviation compared with the previous works.ConclusionsA novel gradient based algorithm has been proposed which is more reliable for the estimation of the ridge information for fingerprint OF and is more accurate in preserving the singularity localization. Compared with the previously proposed gradient based methods, the advantages of the proposed RBSF lie in three aspects. Firstly a weighted multi-scale composite window is put forward to replace the single window used by conventional gradient based methods and to adaptively choose the scales of the blocks. Secondly, a hierarchical smoothing strategy is proposed to enhance the OF by using the two-orientation-zone filtering and the three-orientation-zone filtering, aiming to correct the spurious ridges and preserving the genuine location of singular points. Finally, three experiments are designed to test the proposed algorithm together with other popular gradient based methods on real fingerprint images, which are selected from different categories and all are suffering from obvious noise effects. All the experiment results show that the proposed method is superior with respect to reliable OF construction and avoiding singularity localization deviation.

The article describes results of the study of non-traditional hydrocarbon deposits. Reservoir properties of rocks with various types of pores, the dependence of quality of raw materials on the natural conditions of rock formation were analyzed. Problems of exploration and exploitation of non-traditional deposits were identified.

Following a significant production breakthrough in the deep coalbed methane (CBM) reservoir located in the Ordos Basin, developing deep CBM reservoirs has become a strategic focus for various operators across different basins in China. These deep CBM reservoirs exhibit far more intricate intrinsic mechanisms and heterogeneous properties than other unconventional reservoirs. The mineral composition and content in deep CBM are highly diverse. The storage capacity of deep CBM reservoirs is a complex interplay of cleats and various types of pores. The way gas is stored in deep CBM reservoirs is also markedly different from others. These complex and heterogeneous characteristics pose significant challenges in evaluating the mineralogy, porosity, and saturation levels of deep CBM reservoirs. To gain a thorough understanding of these intricate systems, an integration of two-dimensional nuclear magnetic resonance (2D NMR) and gamma-ray spectroscopy measurements was employed in several basins. These techniques were used to characterize the mineral composition, pore structure, porosity, saturation, and gas in place (GIP) for the deep CBM reservoirs. The 2D NMR measurements were employed to characterize pore structures, porosity, saturation, and fluid components using a customized T1-T2 interpretation model. Gamma-ray spectroscopy was used to analyze the mineralogy and perform proximate analysis. The T1-T2 map provided a schematic illustration of fluid components, combined with NMR core analysis, enabling the development of a customized T1-T2 interpretation model. This model facilitated the accurate partitioning of porosity and identification of fluid components, enabling precise estimation of total porosity, effective porosity, gas volume, water volume, saturation, etc. Furthermore, X-ray diffraction analysis on core samples contributed to the establishment of a customized mineral evaluation model. This model enabled the accurate estimation of mineral components, especially for coal, their contents, and proximate analysis metrics, including fixed carbon, ash, volatile matter, and moisture content. By integrating the porosity and saturation data obtained from 2D NMR measurements with the mineral components and proximate analysis results derived from gamma-ray spectroscopy, the studies successfully quantified the GIP for the deep CBM reservoirs. Meanwhile, this integration provides a comprehensive understanding of the deep CBM reservoir's characteristics. The studies provide a comprehensive characterization of deep CBM reservoirs, detailing their porosity, fluid components, saturation levels, mineral composition, proximate analysis, and GIP. This was achieved through the integrated application of 2D NMR and gamma-ray spectroscopy logging techniques.

Ultra-high performance concrete (UHPC) has a high self-healing capacity and is prone to bursting after exposure to high temperatures due to its characteristics. This work evaluates the damage and improvement of UHPC with coarse aggregates through mechanical properties (compressive strength and ultrasonic pulse velocity), transport properties (water absorption and a chloride diffusion test), and micro-properties such as X-ray diffraction (XRD), Mercury intrusion porosimetry (MIP), and Scanning electronic microscopy (SEM). The result demonstrates that polypropylene (PP) fibers are more suitable for high temperature tests than polyacrylonitrile (PAN) fibers. The result shows that 400 °C is the critical temperature point. With the increase in temperature, the hydration becomes significant, and the internal material phase changes accordingly. Although the total pore volume increased, the percentage of various types of pores was optimized within 400 °C. The mass loss gradually increased and the ultrasonic pulse velocity gradually decreased. While the compressive strength first increased and then decreased, and the increase occurred within 25–400 °C. As for the transport properties, the chloride migration coefficient and capillary absorption coefficient both increased dramatically due to the higher sensitivity to temperature changes. The results of the property improvement test showed that at temperatures above 800 °C, the compressive strength recovered by more than 65% and the ultrasonic pulse velocity recovered by more than 75%. In terms of transport properties, compared to the results before self-healing, the chloride migration coefficient decreased by up to 59%, compared with 89% for the capillary absorption coefficient, after self-healing at 800 °C. With respect to the enhancement effect after exposure to high temperatures, the environment of a 5% Na2SO4 solution was not as good as the clean water environment. The corresponding changes in microstructure during the high temperatures and the self-healing process can explain the change in the pattern of macroscopic properties more precisely.

Characterization of interconnectivity, size distribution and uniformity of air voids in porous asphalt concrete using X-ray CT scanning images

There are various approaches available in literature to asses mechanical properties of porous and/or heterogeneous rocks. However, engineers are always confused which one to use for designing structures in rock masses since these approaches do not always provide unique values. In this article, we first review the fundamental concepts of various approaches for modelling porous and/or heterogeneous rocks as equivalent continua. Then, the applications of these approaches to rocks having various types of pores or inclusions are compared and their validity is discussed.

Fingerprint recognition is very stable, reliable, and oldest biometric authentication technology that has been widely used in various civilian and government applications. Like other authentication technologies, fingerprint recognition is not totally free from spoofing. A fingerprint recognition system can be spoofed by placing artificially generated fingerprints of enrolled user at the sensor that are made up of materials like gelatine, silicon, wax or play-doh etc. Liveness detection is a technique to enhance the security of such systems by detecting physiological life signs in fingerprint samples to differentiate a real fingerprint from an artificial one. A live fingerprint image has the property to produce gray level variations along ridges due to presence of sweat pores, perspiration and skin quality (dry, wet) whereas a fake fingerprint has uniform gray levels that can give a fair idea about the liveness in input sample. In the proposed work, two fingerprints are captured by scanner at different time points (0 sec and 2 sec). In order to find their gray level variations, a gray level co-occurrence matrix of each fingerprint image is determined. After that, contrast value is computed from each matrix. If the difference in their contrast value is greater than threshold, then the input fingerprint sample is considered as live otherwise it is considered as fake. Liveness detection solution presented in this work helps to avoid direct attacks at fingerprint sensor that result in very secure and precise fingerprint detection. Keywords: Biometrics, Fingerprint detection, Liveness Detection, Spoofing

Source localization is a fundamental problem in seismology. Current localization techniques often rely on homogeneous isotropic models, even if a survey region is known to be geologically complex or anisotropic. We investigated a model’s influence on localization, using data from a hydraulic injection experiment at the Continental Deep Drilling (Kontinentale Tiefbohrung, or KTB) site in Germany. We performed the localization with a grid-search algorithm and two additional methods for verification. From previous work, a homogeneous isotropic model based on a borehole check shot and a heterogeneous 3D isotropic model were available. Vertical seismic profiling (VSP) and borehole data as well as laboratory data from rock samples provided homogeneous anisotropic models. Although localization with the isotropic and anisotropic models led to the same magnitude of residuals and therefore to a comparable quality of fit, the distribution of the event cloud differed significantly for isotropic and anisotropic media. The isotropic model leads to a southward lateral shift of the event cloud of about [Formula: see text] from the injection point; the cloud appears almost centered at the injection point for the anisotropic models. This implies the classical diffusion concept for event generation, whereas isotropic models suggest the existence of a fault system providing a migration path for injected fluid, generating seismicity at an offset from the injection point. These results indicate that meticulous model building is essential for seismic-event localization and subsequent interpretation.

Rock electrical parameters of tight gas sandstone show large variations in the T2 member in Dingbei Block, Ordos Basin, China. Applying the same rock electrical parameters in water saturation calculations would lead to large errors. Based on casting thin sections, x-ray diffraction, scanning electron microscopy (SEM), cathode luminescence, porosity and permeability, image analysis, and high-pressure mercury intrusion/withdrawal method, identification of the diagenetic facies are first conducted, and then their pore structure and their relationship with rock electrical parameters are investigated.Five diagenetic facies (A–E), which are identified based mainly on pore types and authigenic minerals, have different pore structure and rock electrical parameters. Conceptual models that incorporate the rock properties of each diagenetic facies have been built, before applying the electrical efficiency theory to explain the values of cementation exponent (m) and saturation exponent (n). A conventional network model, a shunt pore model, a netted pore model, and a dotted line model are utilized to mimic the intergranular pores, authigenic kaolinite intercrystal pores, carbonate-cement dissolution pores, and clay-matrix intercrystal pores, respectively. A decrease of the contents of large pores increases electrical efficiency and therefore reduces m. The saturation exponent, which depends on the distribution of water and gas, can be better understood by applying the different pore models. In the shunt and netted pore models, gas displacement starts from the larger pores and smaller pores provide alternative conduction pathways, hence sustaining electrical efficiency and decreasing n. Clay-matrix intercrystal pores are mainly micropores, since the brine in the rocks are isolated after gas displacement, reducing overall electrical efficiency and dramatically increasing the value of n in the diagenetic facies, which is dominated by clay-matrix intercrystal pores.

Fingerprint recognition using a joint transform correlator (JTC) is the most well-known technology among optical fingerprint recognition methods. The JTC method optically compares the reference fingerprint image with the sample fingerprint image then examines match or non-match by acquiring a correlation peak. In contrast to the JTC method, this paper presents a new method to examine fingerprint recognition by producing a computer generated hologram (CGH) of those two fingerprint images and directly comparing them. As a result, we present some parameters to show that fingerprint recognition capability of the CGH direct comparison method is superior to that of the JTC method.

Fingerprint recognition has been used for person identification for centuries, and fingerprint features are divided into three levels. The level 3 feature is the fingerprint pore, which can be used to improve the performance of the automatic fingerprint recognition performance and to prevent spoofing in high-resolution fingerprints. Therefore, the accurate extraction of fingerprint pores is quite important. With the development of convolutional neural networks (CNNs), researchers have made great progress in fingerprint feature extraction. However, these supervised-based methods require manually labelled pores to train the network, and labelling pores is very tedious and time consuming because there are hundreds of pores in one fingerprint. In this paper, we design a weakly supervised pore extraction method that avoids manual label processing and trains the network with a noisy label. This method can achieve results comparable with a supervised CNN-based method.