The Experts below are selected from a list of 188214 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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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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a model based method for the computation of fingerprints orientation Field
IEEE Transactions on Image Processing, 2004Co-Authors: Jie ZhouAbstract:As a global feature of fingerprints, the orientation Field is very important for automatic fingerprint recognition. Many algorithms have been proposed for orientation Field Estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a model-based method for the computation of orientation Field is proposed. First a combination model is established for the representation of the orientation Field by considering its smoothness except for several singular points, in which a polynomial model is used to describe the orientation Field globally and a point-charge model is taken to improve the accuracy locally at each singular point. When the coarse Field is computed by using the gradient-based algorithm, a further result can be gained by using the model for a weighted approximation. Due to the global approximation, this model-based orientation Field Estimation algorithm has a robust performance on different fingerprint images. A further experiment shows that the performance of a whole fingerprint recognition system can be improved by applying this algorithm instead of previous orientation Estimation methods.
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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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: 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.
Jianjiang Feng - One of the best experts on this subject based on the ideXlab platform.
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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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latent fingerprint enhancement via robust orientation Field Estimation
International Journal of Central Banking, 2011Co-Authors: 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.
Jean-philippe Thiran - One of the best experts on this subject based on the ideXlab platform.
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Active deformation Fields: Dense deformation Field Estimation for atlas-based segmentation using the active contour framework
Medical Image Analysis, 2011Co-Authors: Sai Subrahmanyam Gorthi, Claudio Pollo, Valerie Duay, Xavier Bresson, Meritxell Bach Cuadra, F. Javier Sánchez Castro, Abdelkarim S. Allal, Jean-philippe ThiranAbstract:This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation Fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.
Valerie Duay - One of the best experts on this subject based on the ideXlab platform.
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Active deformation Fields: Dense deformation Field Estimation for atlas-based segmentation using the active contour framework
Medical Image Analysis, 2011Co-Authors: Sai Subrahmanyam Gorthi, Claudio Pollo, Valerie Duay, Xavier Bresson, Meritxell Bach Cuadra, F. Javier Sánchez Castro, Abdelkarim S. Allal, Jean-philippe ThiranAbstract:This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation Fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.
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dense deformation Field Estimation for atlas based segmentation of pathological mr brain images
Computer Methods and Programs in Biomedicine, 2006Co-Authors: Bach M Cuadra, Mathieu De Craene, Claudio Pollo, Valerie Duay, Ph J ThiranAbstract:Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation Field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.