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Xu W, Luo J, Zhu C, Lu W, Zeng J, Shi S, Lin C. Document images forgery localization using a two‐stream network. INT J INTELL SYST 2021. [DOI: 10.1002/int.22792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Wenbo Xu
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat‐sen University Guangzhou China
| | - Junwei Luo
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat‐sen University Guangzhou China
| | - Chuntao Zhu
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat‐sen University Guangzhou China
| | - Wei Lu
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat‐sen University Guangzhou China
| | | | | | - Cong Lin
- School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou China
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2
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Image retrieval based on aggregated deep features weighted by regional significance and channel sensitivity. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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3
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Ghafoor M, Tariq SA, Zia T, Taj IA, Abbas A, Hassan A, Zomaya AY. Fingerprint Identification With Shallow Multifeature View Classifier. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:4515-4527. [PMID: 31880579 DOI: 10.1109/tcyb.2019.2957188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article presents an efficient fingerprint identification system that implements an initial classification for search-space reduction followed by minutiae neighbor-based feature encoding and matching. The current state-of-the-art fingerprint classification methods use a deep convolutional neural network (DCNN) to assign confidence for the classification prediction, and based on this prediction, the input fingerprint is matched with only the subset of the database that belongs to the predicted class. It can be observed for the DCNNs that as the architectures deepen, the farthest layers of the network learn more abstract information from the input images that result in higher prediction accuracies. However, the downside is that the DCNNs are data hungry and require lots of annotated (labeled) data to learn generalized network parameters for deeper layers. In this article, a shallow multifeature view CNN (SMV-CNN) fingerprint classifier is proposed that extracts: 1) fine-grained features from the input image and 2) abstract features from explicitly derived representations obtained from the input image. The multifeature views are fed to a fully connected neural network (NN) to compute a global classification prediction. The classification results show that the SMV-CNN demonstrated an improvement of 2.8% when compared to baseline CNN consisting of a single grayscale view on an open-source database. Moreover, in comparison with the state-of-the-art residual network (ResNet-50) image classification model, the proposed method performs comparably while being less complex and more efficient during training. The result of classification-based fingerprint identification has shown that the search space is reduced by over 50% without degradation of identification accuracies.
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Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons. Symmetry (Basel) 2021. [DOI: 10.3390/sym13050750] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.
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5
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Peralta D, Saeys Y. Robust unsupervised dimensionality reduction based on feature clustering for single-cell imaging data. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106421] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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6
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Fingerprint Classification through Standard and Weighted Extreme Learning Machines. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10124125] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fingerprint classification is a stage of biometric identification systems that aims to group fingerprints and reduce search times and computational complexity in the databases of fingerprints. The most recent works on this problem propose methods based on deep convolutional neural networks (CNNs) by adopting fingerprint images as inputs. These networks have achieved high classification performances, but with a high computational cost in the network training process, even by using high-performance computing techniques. In this paper, we introduce a novel fingerprint classification approach based on feature extractor models, and basic and modified extreme learning machines (ELMs), being the first time that this approach is adopted. The weighted ELMs naturally address the problem of unbalanced data, such as fingerprint databases. Some of the best and most recent extractors (Capelli02, Hong08, and Liu10), which are based on the most relevant visual characteristics of the fingerprint image, are considered. Considering the unbalanced classes for fingerprint identification schemes, we optimize the ELMs (standard, original weighted, and decay weighted) in terms of the geometric mean by estimating their hyper-parameters (regularization parameter, number of hidden neurons, and decay parameter). At the same time, the classic accuracy and penetration-rate metrics are computed for comparison purposes with the superior CNN-based methods reported in the literature. The experimental results show that weighted ELM with the presence of the golden-ratio in the weighted matrix (W-ELM2) overall outperforms the rest of the ELMs. The combination of the Hong08 extractor and W-ELM2 competes with CNNs in terms of the fingerprint classification efficacy, but the ELMs-based methods have been demonstrated their extremely fast training speeds in any context.
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Bécue A, Eldridge H, Champod C. Interpol review of fingermarks and other body impressions 2016-2019. Forensic Sci Int Synerg 2020; 2:442-480. [PMID: 33385142 PMCID: PMC7770454 DOI: 10.1016/j.fsisyn.2020.01.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 01/16/2020] [Indexed: 12/17/2022]
Abstract
This review paper covers the forensic-relevant literature in fingerprint and bodily impression sciences from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20 Review%20 Papers%202019. pdf.
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Affiliation(s)
- Andy Bécue
- École des Sciences Criminelles, Faculté de Droit, des Sciences criminelles et d’Administration publique, Quartier Sorge, Building Batochime, University of Lausanne, CH-1015, Lausanne, Dorigny, Switzerland
| | - Heidi Eldridge
- École des Sciences Criminelles, Faculté de Droit, des Sciences criminelles et d’Administration publique, Quartier Sorge, Building Batochime, University of Lausanne, CH-1015, Lausanne, Dorigny, Switzerland
| | - Christophe Champod
- École des Sciences Criminelles, Faculté de Droit, des Sciences criminelles et d’Administration publique, Quartier Sorge, Building Batochime, University of Lausanne, CH-1015, Lausanne, Dorigny, Switzerland
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8
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Progressive Cascaded Convolutional Neural Networks for Single Tree Detection with Google Earth Imagery. REMOTE SENSING 2019. [DOI: 10.3390/rs11151786] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
High-resolution remote sensing images can not only help forestry administrative departments achieve high-precision forest resource surveys, wood yield estimations and forest mapping but also provide decision-making support for urban greening projects. Many scholars have studied ways to detect single trees from remote sensing images and proposed many detection methods. However, the existing single tree detection methods have many errors of commission and omission in complex scenes, close values on the digital data of the image for background and trees, unclear canopy contour and abnormal shape caused by illumination shadows. To solve these problems, this paper presents progressive cascaded convolutional neural networks for single tree detection with Google Earth imagery and adopts three progressive classification branches to train and detect tree samples with different classification difficulties. In this method, the feature extraction modules of three CNN networks are progressively cascaded, and the network layer in the branches determined whether to filter the samples and feed back to the feature extraction module to improve the precision of single tree detection. In addition, the mechanism of two-phase training is used to improve the efficiency of model training. To verify the validity and practicability of our method, three forest plots located in Hangzhou City, China, Phang Nga Province, Thailand and Florida, USA were selected as test areas, and the tree detection results of different methods, including the region-growing, template-matching, convolutional neural network and our progressive cascaded convolutional neural network, are presented. The results indicate that our method has the best detection performance. Our method not only has higher precision and recall but also has good robustness to forest scenes with different complexity levels. The F1 measure analysis in the three plots was 81.0%, which is improved by 14.5%, 18.9% and 5.0%, respectively, compared with other existing methods.
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Wang G, Gao J. Parallel conjugate gradient-particle swarm optimization and the parameters design based on the polygonal fuzzy neural network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-182882] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Guijun Wang
- School of Mathematical Science, Tianjin Normal University, Tianjin, China
| | - Jiansi Gao
- The Ninth Middle School in Tianjin, Tianjin, China
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Hassan S, Waheed H, Aljohani NR, Ali M, Ventura S, Herrera F. Virtual learning environment to predict withdrawal by leveraging deep learning. INT J INTELL SYST 2019. [DOI: 10.1002/int.22129] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Saeed‐Ul Hassan
- Department of Computer ScienceInformation Technology UniversityLahore Pakistan
| | - Hajra Waheed
- Department of Computer ScienceInformation Technology UniversityLahore Pakistan
| | - Naif R. Aljohani
- Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah Kingdom of Saudi Arabia
| | - Mohsen Ali
- Department of Computer ScienceInformation Technology UniversityLahore Pakistan
| | - Sebastián Ventura
- Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah Kingdom of Saudi Arabia
- Andalusian Research Institute in Data Science and Computational IntelligenceUniversity of CórdobaCórdoba Spain
| | - Francisco Herrera
- Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah Kingdom of Saudi Arabia
- Andalusian Research Institute in Data Science and Computational IntelligenceUniversity of GranadaGranada Spain
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Ilankumaran S, Deisy C, Pandian R. Quality-based pattern C2 code score-level fusion in multimodal biometric authentication system using pattern net. Soft comput 2019. [DOI: 10.1007/s00500-018-03751-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Tertychnyi P, Ozcinar C, Anbarjafari G. Low‐quality fingerprint classification using deep neural network. IET BIOMETRICS 2018. [DOI: 10.1049/iet-bmt.2018.5074] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Cagri Ozcinar
- School of Computer Science and Statistics, Trinity College DublinDublin 2Ireland
| | - Gholamreza Anbarjafari
- iCV Lab, Institute of Technology, University of TartuTartuEstonia
- Department of Electrical and Electronic Engineering, Hasan Kalyoncu UniversityGaziantepTurkey
- GoSwift Inc.TallinnEstonia
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Zeng F, Hu S, Xiao K. Research on partial fingerprint recognition algorithm based on deep learning. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3609-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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14
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Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study. REMOTE SENSING 2017. [DOI: 10.3390/rs9121220] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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