The Experts below are selected from a list of 181377 Experts worldwide ranked by ideXlab platform
Jie Zhou - One of the best experts on this subject based on the ideXlab platform.
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BTAS - Orientation Field Estimation for Latent Fingerprints by Exhaustive Search of Large Database
2018 IEEE 9th International Conference on Biometrics Theory Applications and Systems (BTAS), 2018Co-Authors: Qihao Yin, Jianjiang Feng, Jie ZhouAbstract:Orientation Field estimation is one of the most important steps in latent fingerprint recognition systems. However, due to very poor image quality, the performances of the state-of-the-art algorithms of Orientation Field estimation are still far from satisfactory. Based on the assumption that a sufficiently large fingerprint database should contain fingerprints whose Orientation Fields are quite similar to a latent fingerprint, we propose an Orientation Field estimation algorithm based on exhaustive search of large database for nearest neighbor. We set up a large database of 10,000 fingerprints of good quality, whose Orientation Fields and poses are estimated offline using traditional methods. Given a latent fingerprint as input, the most similar Orientation Field in the database is found and is combined with the original latent image to obtain the final Orientation Field. As a by product, we also obtain the pose of the latent fingerprint, which can be useful for fingerprint registration. The experimental results on NIST SD27 latent database show our method performs better than the state-of-the-art algorithms, in both Orientation Field estimation accuracy and identification performance, especially on those fingerprints of very poor quality.
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ICB - Multi-scale dictionaries based fingerprint Orientation Field estimation
2016 International Conference on Biometrics (ICB), 2016Co-Authors: Chunjie Chen, Jianjiang Feng, Jie ZhouAbstract:Orientation Field estimation is significantly important for fingerprint recognition. Dictionary based algorithm and its variant, localized dictionaries based algorithm have shown promising performance. In this paper, we extend the original dictionary based algorithm to a multi-scale version. The motivation is that small scale dictionary is more accurate while large scale dictionary is more robust against image noise. Hence information from Orientation Fields of different scales can be integrated to obtain better results. A multi-layer MRF model is used to formulate and solve the proposed problem. Experimental results on challenging latent fingerprint database demonstrate the advantages of the proposed algorithm.
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Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints
IEEE transactions on pattern analysis and machine intelligence, 2014Co-Authors: Xiao Yang, Jianjiang Feng, Jie ZhouAbstract:Dictionary based Orientation Field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. Realizing that ridge Orientations at different locations of fingerprints have different characteristics, we propose a localized dictionaries-based Orientation Field estimation algorithm, in which noisy Orientation patch at a location output by a local estimation approach is replaced by real Orientation patch in the local dictionary at the same location. The precondition of applying localized dictionaries is that the pose of the latent fingerprint needs to be estimated. We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all Orientation patches in the latent fingerprint are accumulated. Experimental results on challenging latent fingerprint datasets show the proposed method outperforms previous ones markedly.
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Orientation Field estimation for latent fingerprint enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Jianjiang Feng, Jie Zhou, Anil K. JainAbstract:Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust Orientation Field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional Orientation Field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint Orientation Field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference Orientation patches. which is constructed using a set of true Orientation Fields, and the compatibility constraint between neighboring Orientation patches. Orientation Field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed Orientation Field estimation algorithm over conventional algorithms.
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Reconstructing Orientation Field From Fingerprint Minutiae to Improve Minutiae-Matching Accuracy
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2009Co-Authors: Fanglin Chen, Jie Zhou, Chunyu YangAbstract:Minutiae are very important features for fingerprint representation, and most practical fingerprint recognition systems only store the minutiae template in the database for further usage. The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets. In this paper, we propose a novel algorithm to use minutiae for fingerprint recognition, in which the fingerprint's Orientation Field is reconstructed from minutiae and further utilized in the matching stage to enhance the system's performance. First, we produce ldquovirtualrdquo minutiae by using interpolation in the sparse area, and then use an Orientation model to reconstruct the Orientation Field from all ldquorealrdquo and ldquovirtualrdquo minutiae. A decision fusion scheme is used to combine the reconstructed Orientation Field matching with conventional minutiae-based matching. Since Orientation Field is an important global feature of fingerprints, the proposed method can obtain better results than conventional methods. Experimental results illustrate its effectiveness.
Mathis Plapp - One of the best experts on this subject based on the ideXlab platform.
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grain coarsening in two dimensional phase Field models with an Orientation Field
Physical Review E, 2017Co-Authors: Balint Korbuly, Tamas Pusztai, Herve Henry, Mathis Plapp, Markus Apel, Laszlo GranasyAbstract:In the literature, contradictory results have been published regarding the form of the limiting (long-time) grain size distribution (LGSD) that characterizes the late stage grain coarsening in two-dimensional and quasi-two-dimensional polycrystalline systems. While experiments and the phase-Field crystal (PFC) model (a simple dynamical density functional theory) indicate a log-normal distribution, other works including theoretical studies based on conventional phase-Field simulations that rely on coarse grained Fields, like the multi-phase-Field (MPF) and Orientation Field (OF) models, yield significantly different distributions. In a recent work, we have shown that the coarse grained phase-Field models (whether MPF or OF) yield very similar limiting size distributions that seem to differ from the theoretical predictions. Herein, we revisit this problem, and demonstrate in the case of OF models [R. Kobayashi, J. A. Warren, and W. C. Carter, Physica D 140, 141 (2000)PDNPDT0167-278910.1016/S0167-2789(00)00023-3; H. Henry, J. Mellenthin, and M. Plapp, Phys. Rev. B 86, 054117 (2012)PRBMDO1098-012110.1103/PhysRevB.86.054117] that an insufficient resolution of the small angle grain boundaries leads to a log-normal distribution close to those seen in the experiments and the molecular scale PFC simulations. Our paper indicates, furthermore, that the LGSD is critically sensitive to the details of the evaluation process, and raises the possibility that the differences among the LGSD results from different sources may originate from differences in the detection of small angle grain boundaries.
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Orientation Field models for polycrystalline solidification grain coarsening and complex growth forms
Journal of Crystal Growth, 2017Co-Authors: Balint Korbuly, Tamas Pusztai, Gyula Toth, Herve Henry, Mathis Plapp, Laszlo GranasyAbstract:Abstract We compare two versions of the phase-Field theory for polycrystalline solidification, both relying on the concept of Orientation Fields: one by Kobayashi et al. [Physica D 140 (2000) 141] [15] and the other by Henry et al. [Phys. Rev. B 86 (2012) 054117] [22]. Setting the model parameters so that the grain boundary energies and the time scale of grain growth are comparable in the two models, we first study the grain coarsening process including the limiting grain size distribution, and compare the results to those from experiments on thin films, to the models of Hillert, and Mullins, and to predictions by multiphase-Field theories. Next, following earlier work by Granasy et al. [Phys. Rev. Lett. 88 (2002) 206105; Phys. Rev. E 72 (2005) 011605] [17,21], we extend the Orientation Field to the liquid state, where the Orientation Field is made to fluctuate in time and space, and employ the model for describing of multi-dendritic solidification, and polycrystalline growth, including the formation of “dizzy” dendrites disordered via the interaction with foreign particles.
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Orientation-Field model for polycrystalline solidification with a singular coupling between order and Orientation
Physical Review B, 2012Co-Authors: Herve Henry, Jesper Mellenthin, Mathis PlappAbstract:The solidification of polycrystalline materials can be modeled by Orientation-Field models, which are formulated in terms of two continuous Fields: a phase Field that describes the thermodynamic state and an Orientation Field that indicates the local direction of the crystallographic axes. The free-energy functionals of existing models generally contain a term proportional to the modulus of the Orientation gradient, which complicates their mathematical analysis and induces artificial long-range interactions between grain boundaries. We present an alternative model in which only the square of the Orientation gradient appears, but in which the phase and Orientation Fields are coupled by a singular function that diverges in the solid phase. We show that this model exhibits stable grain boundaries, the interactions of which decay exponentially with their distance. Furthermore, we demonstrate that the anisotropy of the surface energy can be included while preserving the variational structure of the model. Illustrative numerical simulations of two-dimensional examples are also presented.
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Remarks on some open problems in phase-Field modelling of solidification
Philosophical Magazine, 2010Co-Authors: Mathis PlappAbstract:Three different topics in phase-Field modelling of solidification are discussed, with particular emphasis on the limitations of the currently available modelling approaches. First, thin-interface limits of two-sided phase-Field models are examined, and it is shown that the antitrapping current is in general not sufficient to remove all thin-interface effects. Second, Orientation-Field models for polycrystalline solidification are analyzed, and it is shown that the standard relaxational equation of motion for the Orientation Field is incorrect in coherent polycrystalline matter. Third, it is pointed out that the standard procedure of incorporating fluctuations into the phase-Field approach cannot be used in a straightforward way for a quantitative description of nucleation.
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remarks on some open problems in phase Field modelling of solidification
arXiv: Materials Science, 2010Co-Authors: Mathis PlappAbstract:Three different topics in phase-Field modelling of solidification are discussed, with particular emphasis on the limitations of the currently available modelling approaches. First, thin-interface limits of two-sided phase-Field models are examined, and it is shown that the antitrapping current is in general not sufficient to remove all thin-interface effects. Second, Orientation-Field models for polycrystalline solidification are analysed, and it is shown that the standard relaxational equation of motion for the Orientation Field is incorrect in coherent polycrystalline matter. Third, it is pointed out that the standard procedure to incorporate fluctuations into the phase-Field approach cannot be used in a straightforward way for a quantitative description of nucleation.
Anil K. Jain - One of the best experts on this subject based on the ideXlab platform.
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latent Orientation Field estimation via convolutional neural network
International Conference on Biometrics, 2015Co-Authors: Kai Cao, Anil K. JainAbstract:The Orientation Field of a fingerprint is crucial for feature extraction and matching. However, estimation of Orientation Fields in latents is very challenging because latents are usually of poor quality. Inspired by the superiority of convolutional neural networks (ConvNets) for various classification and recognition tasks, we pose latent Orientation Field estimation in a latent patch to a classification problem, and propose a ConvNet based approach for latent Orientation Field estimation. The underlying idea is to identify the Orientation Field of a latent patch as one of a set of representative Orientation patterns. To achieve this, 128 representative Orientation patterns are learnt from a large number of Orientation Fields. For each Orientation pattern, 10,000 fingerprint patches are selected to train the ConvNet. To simulate the quality of latents, texture noise is added to the training patches. Given image patches extracted from a latent, their Orientation patterns are predicted by the trained ConvNet and quilted together to estimate the Orientation Field of the whole latent. Experimental results on NIST SD27 latent database demonstrate that the proposed algorithm outperforms the state-of-the-art Orientation Field estimation algorithms and can boost the identification performance of a state-of-the-art latent matcher by score fusion.
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ICB - Latent Orientation Field estimation via convolutional neural network
2015 International Conference on Biometrics (ICB), 2015Co-Authors: Kai Cao, Anil K. JainAbstract:The Orientation Field of a fingerprint is crucial for feature extraction and matching. However, estimation of Orientation Fields in latents is very challenging because latents are usually of poor quality. Inspired by the superiority of convolutional neural networks (ConvNets) for various classification and recognition tasks, we pose latent Orientation Field estimation in a latent patch to a classification problem, and propose a ConvNet based approach for latent Orientation Field estimation. The underlying idea is to identify the Orientation Field of a latent patch as one of a set of representative Orientation patterns. To achieve this, 128 representative Orientation patterns are learnt from a large number of Orientation Fields. For each Orientation pattern, 10,000 fingerprint patches are selected to train the ConvNet. To simulate the quality of latents, texture noise is added to the training patches. Given image patches extracted from a latent, their Orientation patterns are predicted by the trained ConvNet and quilted together to estimate the Orientation Field of the whole latent. Experimental results on NIST SD27 latent database demonstrate that the proposed algorithm outperforms the state-of-the-art Orientation Field estimation algorithms and can boost the identification performance of a state-of-the-art latent matcher by score fusion.
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is there a fingerprint pattern in the image
International Conference on Biometrics, 2013Co-Authors: Soweon Yoon, Anil K. JainAbstract:A fingerprint Orientation Field has distinct characteristics which can differentiate fingerprints from any other flow patterns: it has a specific number of singular points (cores and deltas), the configuration of singular points follows a certain spatial distribution, and its global shape is like an arch. In this paper, we propose a global fingerprint Orientation Field model, represented in terms of ordinary differential equations, which does not require any prior information such as singular points or Orientation of a fingerprint. Further, the model requires only a small number of polynomial terms to represent the global fingerprint Orientation Field. The coefficients of the model are found subject to the constraints on the total number of singular points (i.e., 0, 2, or 4) in a fingerprint. The proposed model is used to distinguish fingerprint images from non-fingerprint images and altered fingerprints by measuring the abnormality in the Orientation Field of the image.
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Orientation Field estimation for latent fingerprint enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Jianjiang Feng, Jie Zhou, Anil K. JainAbstract:Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust Orientation Field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional Orientation Field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint Orientation Field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference Orientation patches. which is constructed using a set of true Orientation Fields, and the compatibility constraint between neighboring Orientation patches. Orientation Field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed Orientation Field estimation algorithm over conventional algorithms.
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latent fingerprint enhancement via robust Orientation Field estimation
International Journal of Central Banking, 2011Co-Authors: Soweon Yoon, Jianjiang Feng, Anil K. JainAbstract:Latent fingerprints, or simply latents, have been considered as cardinal evidence for identifying and convicting criminals. The amount of information available for identification from latents is often limited due to their poor quality, unclear ridge structure and occlusion with complex background or even other latent prints. We propose a latent fingerprint enhancement algorithm, which expects manually marked region of interest (ROI) and singular points. The core of the proposed algorithm is a robust Orientation Field estimation algorithm for latents. Short-time Fourier transform is used to obtain multiple Orientation elements in each image block. This is followed by a hypothesize-and-test paradigm based on randomized RANSAC, which generates a set of hypothesized Orientation Fields. Experimental results on NIST SD27 latent fingerprint database show that the matching performance of a commercial matcher is significantly improved by utilizing the enhanced latent fingerprints produced by the proposed algorithm.
Herve Henry - One of the best experts on this subject based on the ideXlab platform.
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grain coarsening in two dimensional phase Field models with an Orientation Field
Physical Review E, 2017Co-Authors: Balint Korbuly, Tamas Pusztai, Herve Henry, Mathis Plapp, Markus Apel, Laszlo GranasyAbstract:In the literature, contradictory results have been published regarding the form of the limiting (long-time) grain size distribution (LGSD) that characterizes the late stage grain coarsening in two-dimensional and quasi-two-dimensional polycrystalline systems. While experiments and the phase-Field crystal (PFC) model (a simple dynamical density functional theory) indicate a log-normal distribution, other works including theoretical studies based on conventional phase-Field simulations that rely on coarse grained Fields, like the multi-phase-Field (MPF) and Orientation Field (OF) models, yield significantly different distributions. In a recent work, we have shown that the coarse grained phase-Field models (whether MPF or OF) yield very similar limiting size distributions that seem to differ from the theoretical predictions. Herein, we revisit this problem, and demonstrate in the case of OF models [R. Kobayashi, J. A. Warren, and W. C. Carter, Physica D 140, 141 (2000)PDNPDT0167-278910.1016/S0167-2789(00)00023-3; H. Henry, J. Mellenthin, and M. Plapp, Phys. Rev. B 86, 054117 (2012)PRBMDO1098-012110.1103/PhysRevB.86.054117] that an insufficient resolution of the small angle grain boundaries leads to a log-normal distribution close to those seen in the experiments and the molecular scale PFC simulations. Our paper indicates, furthermore, that the LGSD is critically sensitive to the details of the evaluation process, and raises the possibility that the differences among the LGSD results from different sources may originate from differences in the detection of small angle grain boundaries.
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Orientation Field models for polycrystalline solidification grain coarsening and complex growth forms
Journal of Crystal Growth, 2017Co-Authors: Balint Korbuly, Tamas Pusztai, Gyula Toth, Herve Henry, Mathis Plapp, Laszlo GranasyAbstract:Abstract We compare two versions of the phase-Field theory for polycrystalline solidification, both relying on the concept of Orientation Fields: one by Kobayashi et al. [Physica D 140 (2000) 141] [15] and the other by Henry et al. [Phys. Rev. B 86 (2012) 054117] [22]. Setting the model parameters so that the grain boundary energies and the time scale of grain growth are comparable in the two models, we first study the grain coarsening process including the limiting grain size distribution, and compare the results to those from experiments on thin films, to the models of Hillert, and Mullins, and to predictions by multiphase-Field theories. Next, following earlier work by Granasy et al. [Phys. Rev. Lett. 88 (2002) 206105; Phys. Rev. E 72 (2005) 011605] [17,21], we extend the Orientation Field to the liquid state, where the Orientation Field is made to fluctuate in time and space, and employ the model for describing of multi-dendritic solidification, and polycrystalline growth, including the formation of “dizzy” dendrites disordered via the interaction with foreign particles.
-
Orientation-Field model for polycrystalline solidification with a singular coupling between order and Orientation
Physical Review B, 2012Co-Authors: Herve Henry, Jesper Mellenthin, Mathis PlappAbstract:The solidification of polycrystalline materials can be modeled by Orientation-Field models, which are formulated in terms of two continuous Fields: a phase Field that describes the thermodynamic state and an Orientation Field that indicates the local direction of the crystallographic axes. The free-energy functionals of existing models generally contain a term proportional to the modulus of the Orientation gradient, which complicates their mathematical analysis and induces artificial long-range interactions between grain boundaries. We present an alternative model in which only the square of the Orientation gradient appears, but in which the phase and Orientation Fields are coupled by a singular function that diverges in the solid phase. We show that this model exhibits stable grain boundaries, the interactions of which decay exponentially with their distance. Furthermore, we demonstrate that the anisotropy of the surface energy can be included while preserving the variational structure of the model. Illustrative numerical simulations of two-dimensional examples are also presented.
Hakil Kim - One of the best experts on this subject based on the ideXlab platform.
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pixel level singular point detection from multi scale gaussian filtered Orientation Field
Pattern Recognition, 2010Co-Authors: Changlong Jin, Hakil KimAbstract:Singular point, as a global feature, plays an important role in fingerprint recognition. Inconsistent detection of singular points apparently gives an affect to fingerprint alignment, classification, and verification accuracy. This paper proposes a novel approach to pixel-level singular point detection from the Orientation Field obtained by multi-scale Gaussian filters. Initially, a robust pixel-level Orientation Field is estimated by a multi-scale averaging framework. Then, candidate singular points in pixel-level are extracted from the complex angular gradient plane derived directly from the pixel-level Orientation Field. The candidate singular points are finally validated via a cascade framework comprised of nested Poincare indices and local feature-based classification. Experimental results over the FVC 2000 DB2 confirm that the proposed method achieves robust and accurate Orientation Field estimation and consistent pixel-level singular point detection. The experimental results exhibit a low computational cost with better performance. Thus, the proposed method can be employed in real-time fingerprint recognition.
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High-resolution Orientation Field estimation based on multi-scale Gaussian filter
IEICE Electronics Express, 2009Co-Authors: Changlong Jin, Hakil KimAbstract:Orientation Field plays the most important role in fingerprint recognition. Proposed in this paper is a novel approach of pixel-wise Orientation Field estimation using multi-scale Gaussian filter. A three-stage averaging framework in pixel-scale, block-scale, and Orientation-scale is developed for handling gradient vectors, coherence data, and Orientation vectors, respectively. Experimental results on various FVC datasets show the proposed algorithm achieves accurate Orientation Field estimation which is robust to local defects, such as scar, low contrast, ridge discontinuity, smudged area, etc. with a low computational cost.