Journal articles on the topic 'Fingerprints Classification'
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Saeed, Fahman, Muhammad Hussain, and Hatim A. Aboalsamh. "Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet)." Mathematics 10, no. 8 (April 12, 2022): 1285. http://dx.doi.org/10.3390/math10081285.
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Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing the architecture of a CNN model is a laborious and time-consuming task. We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification; it automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between-class scatter to within-class scatter. It helps to design lightweight CNN models, which are efficient and speed up the fingerprint recognition process. The method was evaluated two public-domain benchmark datasets FingerPass and FVC2004 benchmark datasets, which contain noisy, low-quality fingerprints obtained using live scan devices and cross-sensor fingerprints. The designed models outperform the well-known pre-trained models and the state-of-the-art fingerprint classification techniques.
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Lebedev, D., and A. Abzhalilova. "ALGORITHMS FOR FINGERPRINT CLASSIFICATION." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (February 10, 2021): 39–44. http://dx.doi.org/10.32014/2021.2224-5294.6.
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Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.
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Lebedev, D., and A. Abzhalilova. "ALGORITHMS FOR FINGERPRINT CLASSIFICATION." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (February 8, 2021): 39–44. http://dx.doi.org/10.32014/2021.2518-1726.6.
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Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.
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Tazight, Idriss, and Mohamed Fakir. "Fingerprint Classification Using Fuzzy-neural Network and Other Methods." IAES International Journal of Artificial Intelligence (IJ-AI) 3, no. 3 (September 1, 2014): 129. http://dx.doi.org/10.11591/ijai.v3.i3.pp129-135.
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The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint images is a very complex pattern recognition problem, due to properties of intra-class diversitiesand inter-class similarities. Its objective is to reduce the responsetime and reducing the search space in an automatic identificationsystem fingerprint (AIS), in classifying fingerprints. In these papers we present a system of fingerprint classificationbased on singular characteristics for extracting feature vectorsand neural networks and fuzzy neural networks, SVM and Knearest neighbour for classifying.
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Suwarno, Sri. "Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron." Sinkron 8, no. 1 (January 1, 2023): 139–44. http://dx.doi.org/10.33395/sinkron.v8i1.11925.
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Fingerprint-based gender classification is beneficial for speeding up the fingerprint identification of criminals, accident victims, and natural disaster victims that are difficult to be recognized based on their physical characteristics. The biggest obstacle to digitally classifying fingerprints is the image's poor quality. Some methods have been developed to improve image quality through various preprocessing, such as noise removal, background segmentation, thinning, and binarization. However, as these processes increase the classification time, some methods have been developed to classify fingerprints without preprocessing. One of them that has shown excellent success is CNN (Convolutional Neural Network). The method does not require preprocessing, but the computation time is very long and requires large amounts of training data. This study proposed a new method that did not need any preprocessing by using wavelet decomposition combined with the max-pooling process to generate features. Firstly, the fingerprint image was decomposed with a Haar wavelet of 4 levels, and each level was followed by a max-pooling process with a 2´2 filter. After that, the resulting feature was used as training data for the Multilayer Perceptron (MLP) network. In this study, the training data was a dataset from NIST (National Institute of Standart and Technology), with 750 fingerprints consisting of male and female fingerprints, each as many as 375. The method could produce a total accuracy of 80.1%.
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LIU, LI-MIN, CHING-YU HUANG, and D. C. DOUGLAS HUNG. "A DIRECTIONAL APPROACH TO FINGERPRINT CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 02 (March 2008): 347–65. http://dx.doi.org/10.1142/s0218001408006211.
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In this article, we present a new fingerprint classification algorithm. Singular points are first extracted from enhanced fingerprint direction images with a resolution of 2 × 2 pixels by the modified SEA algorithm. Based on the number of singular points, fingerprints are categorized into types of "arch", "whorl", and "solitary". Solitary fingerprints are properly rotated and then further processed to generate direction patterns that lead to establishment of individual direction template. Direction constraints are formed and derived from pattern descriptors by their structural layout. Decision rules are then established and pattern templates are classified into three more types: "right loop", "left loop", and "tented arch". NIST-4 database was used for an experimental test, and our classification accuracy was 91.62% with 1.55% rejection on the five-class system (94.38% on the four-class system), which is the best result on the five-class system to-date. An additional experiment on NIST-14 database reports 89.15% accuracy with 3.07% rejection.
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Dionson, Mary Gift D., and El Jireh P. Bibangco. "Inception-V3 Architecture in Dermatoglyphics-Based Temperament Classification." Philippine Social Science Journal 3, no. 2 (November 16, 2020): 173–74. http://dx.doi.org/10.52006/main.v3i2.164.
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Personality classification is one of the areas of behavioral psychology that focuses on categorizing individuals. Different factors constitute the main currents of human personality. These factors turned out to be complicated and sometimes yield a biased result. Meanwhile, the entire human body reflects the character of its possessor more accurately than any set of questionnaires. Dermatoglyphics is the scientific study of fingerprints. Fingerprint patterns and ridge density are the viable bases in the classification of the personality of an individual. This uniqueness has expanded through research confirming parents' ability to identify their children's unique potentials through fingerprint analysis. Bridging the gap between computer science and psychology is one of the biggest challenges of the study. Exploring the possibilities revolves around image processing, where fingerprints served as image input and a deep learning convolutional neural network model implemented in the Inception-v3 architecture is used to analyze and classify different fingerprint patterns finally associate with the classified prints to its corresponding temperament type.
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Hariyanto, Hariyanto, Sarifuddin Madenda, Sunny Arief Sudiro, and Tubagus Maulana Kusuma. "Fingerprint Authenticity Classification Algorithm based-on Distance of Minutiae using Convolutional Neural Network." Jurnal Telekomunikasi dan Komputer 11, no. 3 (December 31, 2021): 243. http://dx.doi.org/10.22441/incomtech.v11i3.13770.
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Fingerprint identification systems are vulnerable to attempted authentication fraud by creating fake fingerprints that mimic the live. This paper proposes method to detect whether a fingerprint is live fingerprint or fake fingerprint using Convolutional Neural Network (CNN). We construct a features database of distances among minutiaes of fingerprints, where the distance calculation is based-on Euclidean Distance. Furthermore, the distance features database that has been constructed is classified using the CNN. CNN is a deep learning method designed for machine learning processes so that computers recognize objects in an image and this method has capability classifying an object. The numerical results have shown that the best accuracy achieves 99.38% when the learning rate is 0.001 with the epoch of 100.
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Militello, Carmelo, Leonardo Rundo, Salvatore Vitabile, and Vincenzo Conti. "Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons." Symmetry 13, no. 5 (April 26, 2021): 750. http://dx.doi.org/10.3390/sym13050750.
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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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Mishra, Annapurna, and Satchidananda Dehuri. "Real-time online fingerprint image classification using adaptive hybrid techniques." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4372. http://dx.doi.org/10.11591/ijece.v9i5.pp4372-4381.
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<p class="Abstract">This paper presents three different hybrid classification techniques applied for the first time in real-time online fingerprint classification. Classification of online real time fingerprints is a complex task as it involves adaptation and tuning of classifier parameters for better classification accuracy. To accomplish the optimal adaptation of parameters of functional link artificial neural network (FLANN) for real-time online fingerprint classification, proven and established optimizers, such as Biogeography based optimizer (BBO), Genetic algorithm (GA), and Particle swarm optimizer (PSO) are intelligently infused with it to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter-bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system. Three hybrid classifiers, the optimized weight adapted Biogeography based optimized functional link artificial neural network (BBO-FLANN), Genetic algorithm based functional link artificial neural network (GA-FLANN) and Particle swarm optimized functional link artificial neural network (PSO-FLANN), are explored for real-time online fingerprint classification, where the PSO-FLANN technique is showing superior performance as compared to GA-FLANN and BBO-FLANN techniques. The best accuracy observed by the application of PSO-FLANN, is 98% for real-time online fingerprint classification.</p>
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Ezin, Eugène C. "Pyramidal Structure Algorithm for Fingerprint Classification Based on Artificial Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (January 20, 2010): 63–68. http://dx.doi.org/10.20965/jaciii.2010.p0063.
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Feature extraction plays a primary role in pattern recognition classification. Many context-based and problem-based algorithms have been proposed providing good performance in high-quality fingerprint imaging but fail when declining with poor-quality fingerprints. The pyramidal algorithm we present in this paper operates on an image matrix layer for extracting features from ink-and-paper fingerprints. The effectiveness of the pyramidal algorithm compared to the consolidation algorithm is demonstrated using a backpropagation neural network experiment to test preprocessed data.
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Rowa, Arianti Magi, and Nikmatul Iza. "Profil Fingerprinting (Sidik Jari) pada Populasi Suku Ububewi Di Wanukaka Sumba Barat, Nusa Tenggara Timur, Indonesia." Prosiding Seminar Nasional IKIP Budi Utomo 2, no. 01 (November 13, 2021): 289–95. http://dx.doi.org/10.33503/prosiding.v2i01.1472.
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Fingerprints have unique and permanent properties as differentiators from one individual to another, even in identical twins the fingerprints are not the same, besides that fingerprints can be used as a search tool. This study aims to analyze fingerprint patterns in the population of the Ububewi tribe. This research is a qualitative descriptive study with a population of 70 students from the Ububewi tribe, West Sumba, East Nusa Tenggara. Data collection techniques were carried out by interviewing, filling out questionnaires, and printing fingerprints on reading sheets. The results of the fingerprint pattern were analyzed using the guidelines in the finger classification system. Based on the research, it shows that the characteristic of the fingerprint pattern of the Ububewi tribe is having a dominant ulnar loop (UL) pattern on the middle finger (M) of the right hand by 64.28% (45 fingers) and the little finger (L) of the right and left hands of 72. 86% (51 fingers) and 70% (49 fingers).
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MIN, JUN-KI, and SUNG-BAE CHO. "MULTIPLE DECISION TEMPLATES WITH ADAPTIVE FEATURES FOR FINGERPRINT CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 08 (December 2007): 1323–38. http://dx.doi.org/10.1142/s0218001407006009.
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This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.
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Alsharman, Nesreen, Adeeb Saaidah, Omar Almomani, Ibrahim Jawarneh, and Laila Al-Qaisi. "Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection." Security and Communication Networks 2022 (April 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/4375232.
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Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.
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Zabala-Blanco, David, Marco Mora, Ricardo J. Barrientos, Ruber Hernández-García, and José Naranjo-Torres. "Fingerprint Classification through Standard and Weighted Extreme Learning Machines." Applied Sciences 10, no. 12 (June 15, 2020): 4125. http://dx.doi.org/10.3390/app10124125.
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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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Dawyndt, Peter, Fabiano L. Thompson, Brian Austin, Jean Swings, Timo Koski, and Mats Gyllenberg. "Application of sliding-window discretization and minimization of stochastic complexity for the analysis of fAFLP genotyping fingerprint patterns of Vibrionaceae." International Journal of Systematic and Evolutionary Microbiology 55, no. 1 (January 1, 2005): 57–66. http://dx.doi.org/10.1099/ijs.0.63136-0.
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Minimization of stochastic complexity (SC) was used as a method for classification of genotypic fingerprints. The method was applied to fluorescent amplified fragment length polymorphism (fAFLP) fingerprint patterns of 507 Vibrionaceae representatives. As the current BinClass implementation of the optimization algorithm for classification only works on binary vectors, the original fingerprints were discretized in a preliminary step using the sliding-window band-matching method, in order to maximally preserve the information content of the original band patterns. The novel classification generated using the BinClass software package was subjected to an in-depth comparison with a hierarchical classification of the same dataset, in order to acknowledge the applicability of the new classification method as a more objective algorithm for the classification of genotyping fingerprint patterns. Recent DNA–DNA hybridization and 16S rRNA gene sequence experiments proved that the classification based on SC-minimization forms separate clusters that contain the fAFLP patterns for all representatives of the species Enterovibrio norvegicus, Vibrio fortis, Vibrio diazotrophicus or Vibrio campbellii, while previous hierarchical cluster analysis had suggested more heterogeneity within the fAFLP patterns by splitting the representatives of the above-mentioned species into multiple distant clusters. As a result, the new classification methodology has highlighted some previously unseen relationships within the biodiversity of the family Vibrionaceae.
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Qi, Yong, Mengzhe Qiu, Hefeifei Jiang, and Feiyang Wang. "Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification." Applied Sciences 12, no. 19 (October 10, 2022): 10152. http://dx.doi.org/10.3390/app121910152.
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The fingerprint is an important biological feature of the human body, which contains abundant biometric information. At present, the academic exploration of fingerprint gender characteristics is generally at the level of understanding, and the standardization research is quite limited. A robust approach is presented in this article, Dense Dilated Convolution ResNet Autoencoder, to extract valid gender information from fingerprints. By replacing the normal convolution operations with the atrous convolution in the backbone, prior knowledge is provided to keep the edge details, and the global reception field can be extended. The results were explored from three aspects: (1) Efficiency of DDC-ResNet. We conducted experiments using a combination of 6 typical automatic feature extractors with 9 classifiers for a total of 54 combinations are evaluated in our dataset; the experimental results show that the combination of methods we used achieved an average accuracy of 96.5%, with a classification accuracy of 97.52% for males and 95.48% for females, which outperformed the other experimental combinations. (2) The effect of the finger. The results showed that the right ring finger was the most effective for finger classification by gender. (3) The effect of specific features. We used the Class Activating Mapping method to plot fingerprint concentration thermograms, which allowed us to infer that fingerprint epidermal texture features are related to gender. The results demonstrated that autoencoder networks are a powerful method for extracting gender-specific features to help hide the privacy information of the user’s gender contained in the fingerprint. Our experiments also identified three levels of features in fingerprints that are important for gender differentiation, including loops and whorls shape, bifurcations shape, and line shapes.
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Yi, Junkai, Guanglin Gong, Zeyu Liu, and Yacong Zhang. "Classification of Markov Encrypted Traffic on Gaussian Mixture Model Constrained Clustering." Wireless Communications and Mobile Computing 2021 (October 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/4935108.
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In order to solve the problem that traditional analysis approaches of encrypted traffic in encryption transmission of network application only consider the traffic classification in the complete communication process with ignoring traffic classification in the simplified communication process, and there are a lot of duplication problems in application fingerprints during state transition, a new classification approach of encrypted traffic is proposed. The article applies the Gaussian mixture model (GMM) to analyze the length of the message, and the model is established to solve the problem of application fingerprint duplication. The fingerprints with similar lengths of the same application are divided into as few clusters as possible by constrained clustering approach, which speeds up convergence speed and improves the clustering effect. The experimental results show that compared with the other encryption traffic classification approaches, the proposed approach has 11.7%, 19.8%, 6.86%, and 5.36% improvement in TPR, FPR, Precision, and Recall, respectively, and the classification effect of encrypted traffic is significantly improved.
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Luda, M. P., N. Li Pira, D. Trevisan, and V. Pau. "Evaluation of Antifingerprint Properties of Plastic Surfaces Used in Automotive Components." International Journal of Polymer Science 2018 (November 28, 2018): 1–11. http://dx.doi.org/10.1155/2018/1895683.
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The antifingerprint properties of a range of surfaces produced with different technologies (in-mould decoration, in-mould labeling, and painted) were objectively evaluated by depositing on them in standard conditions an artificial fingerprint for direct determination of its visibility. The artificial fingerprint behaves similarly to the real human fingerprints. A classification method is then proposed to classify surfaces on the base of antifingerprint properties by measuring the roughness profile (Ra) and calculating the % variation of gloss (GU 20 and 60°), haze, luminance (L), and diffuse reflectance (R) values after fingerprint deposition. This approach provides an objective and quantitative test method to determine visual antifingerprint properties of coated surfaces, instead of the “easy-to-clean” properties commonly evaluated. The data acquired provides a design guideline for fabricating visually fingerprint-free surfaces by controlling roughness, texture, color, and transparency of surfaces, with the aim of optically masking fingerprints.
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Yang, Jun-Ho, and Jack J. Yoh. "Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches." Applied Spectroscopy 72, no. 7 (March 23, 2018): 1047–56. http://dx.doi.org/10.1177/0003702818765183.
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A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.
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Li, Xinting, Weijin Cheng, Chengsheng Yuan, Wei Gu, Baochen Yang, and Qi Cui. "Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices." Mathematics 8, no. 4 (April 3, 2020): 517. http://dx.doi.org/10.3390/math8040517.
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Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported.
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T Abraham, Ajitha, and Yasim Khan M. "Age classification from fingerprints – wavelet approach." International Journal on Cybernetics & Informatics 5, no. 2 (April 30, 2016): 265–74. http://dx.doi.org/10.5121/ijci.2016.5229.
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He, Zhengfang, Ivy Kim D. Machica, Jan Carlo T. Arroyo, Ma Luche P. Sabayle, Weibin Su, Gang Xu, Yikai Wang, Mingbo Pan, and Allemar Jhone P. Delima. "Fingerprint classification combined with Gabor filter and convolutional neural network." International Journal of ADVANCED AND APPLIED SCIENCES 10, no. 1 (January 2023): 69–76. http://dx.doi.org/10.21833/ijaas.2023.01.010.
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A fingerprint is an impression left by the friction ridges of a human finger. A fingerprint classification system groups fingerprint according to their characteristics and therefore helps to match a fingerprint against an extensive database of fingerprints. The Henry classification system is widely used among fingerprint classification systems. Some researchers have used traditional machine learning or deep learning for fingerprint classification. Nevertheless, traditional algorithms cannot extract the depth features of the fingerprint, and most deep learning algorithms lack fingerprint image enhancement. So, this paper combined the Gabor Filter and Convolutional Neural Network to extract fingerprint features. The model has two channels, one is a Deep Convolutional Neural Network (DCNN), and the other is a Shallow Convolutional Neural Network (SCNN). The DCNN consists of a neural network with eight layers, which can extract deep features of the fingerprint. The SCNN consists of Gabor Filter and a neural network with two layers that can extract features from clear fingerprint images. This paper uses NIST Special Database 4 for experiments. Experimental results show that the model proposed in this paper has achieved 91.4% accuracy. Compared with other algorithms, this model has higher accuracy than others. It shows that combined with the Gabor Filter and Convolutional Neural Network can better extract the ridge features of fingerprint images.
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Seurinck, Sylvie, Willy Verstraete, and Steven D. Siciliano. "Use of 16S-23S rRNA Intergenic Spacer Region PCR and Repetitive Extragenic Palindromic PCR Analyses of Escherichia coli Isolates To Identify Nonpoint Fecal Sources." Applied and Environmental Microbiology 69, no. 8 (August 2003): 4942–50. http://dx.doi.org/10.1128/aem.69.8.4942-4950.2003.
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ABSTRACT Despite efforts to minimize fecal input into waterways, this kind of pollution continues to be a problem due to an inability to reliably identify nonpoint sources. Our objective was to find candidate source-specific Escherichia coli fingerprints as potential genotypic markers for raw sewage, horses, dogs, gulls, and cows. We evaluated 16S-23S rRNA intergenic spacer region (ISR)-PCR and repetitive extragenic palindromic (rep)-PCR analyses of E. coli isolates as tools to identify nonpoint fecal sources. The BOXA1R primer was used for rep-PCR analysis. A total of 267 E. coli isolates from different fecal sources were typed with both techniques. E. coli was found to be highly diverse. Only two candidate source-specific E. coli fingerprints, one for cow and one for raw sewage, were identified out of 87 ISR fingerprints. Similarly, there was only one candidate source-specific E. coli fingerprint for horse out of 59 BOX fingerprints. Jackknife analysis resulted in an average rate of correct classification (ARCC) of 83% for BOX-PCR analysis and 67% for ISR-PCR analysis for the five source categories of this study. When nonhuman sources were pooled so that each isolate was classified as animal or human derived (raw sewage), ARCCs of 82% for BOX-PCR analysis and 72% for ISR-PCR analysis were obtained. Critical factors affecting the utility of these methods, namely sample size and fingerprint stability, were also assessed. Chao1 estimation showed that generally 32 isolates per fecal source individual were sufficient to characterize the richness of the E. coli population of that source. The results of a fingerprint stability experiment indicated that BOX and ISR fingerprints were stable in natural waters at 4, 12, and 28°C for 150 days. In conclusion, 16S-23S rRNA ISR-PCR and rep-PCR analyses of E. coli isolates have the potential to identify nonpoint fecal sources. A fairly small number of isolates was needed to find candidate source-specific E. coli fingerprints that were stable under the simulated environmental conditions.
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Johnson, LeeAnn K., Mary B. Brown, Ethan A. Carruthers, John A. Ferguson, Priscilla E. Dombek, and Michael J. Sadowsky. "Sample Size, Library Composition, and Genotypic Diversity among Natural Populations of Escherichia coli from Different Animals Influence Accuracy of Determining Sources of Fecal Pollution." Applied and Environmental Microbiology 70, no. 8 (August 2004): 4478–85. http://dx.doi.org/10.1128/aem.70.8.4478-4485.2004.
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ABSTRACT A horizontal, fluorophore-enhanced, repetitive extragenic palindromic-PCR (rep-PCR) DNA fingerprinting technique (HFERP) was developed and evaluated as a means to differentiate human from animal sources of Escherichia coli. Box A1R primers and PCR were used to generate 2,466 rep-PCR and 1,531 HFERP DNA fingerprints from E. coli strains isolated from fecal material from known human and 12 animal sources: dogs, cats, horses, deer, geese, ducks, chickens, turkeys, cows, pigs, goats, and sheep. HFERP DNA fingerprinting reduced within-gel grouping of DNA fingerprints and improved alignment of DNA fingerprints between gels, relative to that achieved using rep-PCR DNA fingerprinting. Jackknife analysis of the complete rep-PCR DNA fingerprint library, done using Pearson's product-moment correlation coefficient, indicated that animal and human isolates were assigned to the correct source groups with an 82.2% average rate of correct classification. However, when only unique isolates were examined, isolates from a single animal having a unique DNA fingerprint, Jackknife analysis showed that isolates were assigned to the correct source groups with a 60.5% average rate of correct classification. The percentages of correctly classified isolates were about 15 and 17% greater for rep-PCR and HFERP, respectively, when analyses were done using the curve-based Pearson's product-moment correlation coefficient, rather than the band-based Jaccard algorithm. Rarefaction analysis indicated that, despite the relatively large size of the known-source database, genetic diversity in E. coli was very great and is most likely accounting for our inability to correctly classify many environmental E. coli isolates. Our data indicate that removal of duplicate genotypes within DNA fingerprint libraries, increased database size, proper methods of statistical analysis, and correct alignment of band data within and between gels improve the accuracy of microbial source tracking methods.
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Vibert, Benoit, Jean-Marie Le Bars, Christophe Charrier, and Christophe Rosenberger. "Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems." Sensors 20, no. 18 (September 21, 2020): 5410. http://dx.doi.org/10.3390/s20185410.
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Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets.
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Chen, Jiong, Heng Zhao, Zhicheng Cao, Fei Guo, and Liaojun Pang. "A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection." Applied Sciences 10, no. 11 (June 2, 2020): 3868. http://dx.doi.org/10.3390/app10113868.
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As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.
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Karsch, Nicholas, Hendrik Schulte, Thomas Musch, and Christoph Baer. "A Novel Localization System in SAR-Demining Applications Using Invariant Radar Channel Fingerprints." Sensors 22, no. 22 (November 10, 2022): 8688. http://dx.doi.org/10.3390/s22228688.
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In this paper, we present a novel two dimensional (2D) frequency-modulated continuous-wave (FMCW) localization method for handheld systems based on the extraction of distinguishable subchannel fingerprints. Compared with other concepts, only one subdivided radar source channel is needed in order to instantly map a one-dimensional measurement to higher-dimensional space coordinates. The additional information of the detected target is implemented with low-cost hardware component features, which exhibit distinguishable space-dependent fingerprint codes. Using the given a priori information of the hardware thus leads to a universally applicable extension for low-cost synthetic aperture radar (SAR)-demining purposes. In addition to the description of the system concept and its requirements, the signal processing steps and the hardware components are presented. Furthermore, the 2D localization accuracy of the system and the classification accuracy of the frequency-coded fingerprints are described in a defined test environment to proof the operational reliability of the realized setup, reaching a classification accuracy of 94.7% and an averaged localization error of 4.9 mm.
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Saeed, Sania, Hassan Dawood, Rubab Mehboob, and Hussain Dawood. "Integration of Probability Based Ridge Variation Information with Local Ridge Orientation for Fingerprint Liveness Detection." Vol 4 Issue 1 4, no. 1 (February 27, 2022): 189–200. http://dx.doi.org/10.33411/ijist/2022040114.
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Fingerprints are commonly used in biometric systems. However, the authentication of these systems became an open challenge because fingerprints can easily be fabricated. In this paper, a hybrid feature extraction approach named Integration of Probability Weighted Spatial Gradient with Ridge Orientation (IPWSGRo) has been proposed for fingerprint liveness detection. IPWSGRo integrates intensity variation and local ridge orientation information. Intensity variation is computed by using probability-weighted moments (PWM) and second order directional derivative filter. Moreover, the ridge orientation is estimated using rotation invariant Local Phase Quantization (LPQri) by retaining only the significant frequency components. These two feature vectors are quantized into predefined intervals to plot a 2-D histogram. The support vector machine classifier (SVM) is then used to determine the validity of fingerprints as either live or spoof. Results are obtained by applying the proposed technique on three standard databases of LivDet competition 2011, 2013, and 2015. Experimental results indicate that the proposed method is able to reduce the average classification error rates (ACER) to 5.7, 2.1, and 5.17% on LivDet2011, 2013, and 2015, respectively.
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Wang, Yuhang, Kun Zhao, Zhengqi Zheng, Wenqing Ji, Shuai Huang, and Difeng Ma. "Indoor Positioning with CNN and Path-Loss Model Based on Multivariable Fingerprints in 5G Mobile Communication System." Sensors 22, no. 9 (April 21, 2022): 3179. http://dx.doi.org/10.3390/s22093179.
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Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.
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Le, Ngoc Tuyen, Duc Huy Le, Jing-Wein Wang, and Chih-Chiang Wang. "Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection." Entropy 21, no. 8 (August 12, 2019): 786. http://dx.doi.org/10.3390/e21080786.
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Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.
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Sheng, Cui Dong, Jia Hong Yu, Lai Han Qing, Wang Zhao Hui, and Mao Xue Fei. "Geographical specificity of fatty acid and multi-element fingerprints of soybean in northern China." Quality Assurance and Safety of Crops & Foods 12, no. 3 (September 21, 2020): 126–39. http://dx.doi.org/10.15586/qas.v12i3.767.
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Soybean is an important food crop in China. Recently, crops cultivated in specific geographical locations have started attracting high prices. Therefore, developing a technique to identify the geographical origin of a crop is crucial to prevent fraud. In this work, we measured the contents of five fatty acids and 17 elements in soybean samples produced in Heilongjiang, the Inner Mongolia Autonomous Region, Jilin and Liaoning using gas chromatography and inductively coupled plasma mass spectrometry. Correlation analysis, principal component analysis and cluster analysis were used to identify the relationship between the metabolic fingerprint and the geographical location. Our results showed a significant correlation between the contents of fatty acids and geographical origin. Principal component analysis provided a preliminary classification of all variables. Hierarchical clustering, based on heat maps, showed that all samples could be classified based on their geographical origins. The model established by partial least squares discriminant analysis showed 89.9% predictive ability, further proving that the 14 classification indexes, comprising fatty acids and elements, could be used as molecular fingerprints to identify and distinguish soybean samples from four different production areas. Besides, pairs of soybean sample fingerprints from the four provinces were compared, and the differences in fatty acid and element contents between the provinces were explained based on the climatic environment and soil distribution. In conclusion, our method of classifying and confirming soybean production areas through fatty acid and multi-element fingerprints can potentially be used for identifying soybean of similar origins.
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Luke, Brian T., Jack R. Collins, Jens K. Habermann, DaRue A. Prieto, Timothy D. Veenstra, and Thomas Ried. "Comparing biomarkers and proteomic fingerprints for classification studies." Journal of Biomedical Science and Engineering 06, no. 04 (2013): 453–65. http://dx.doi.org/10.4236/jbise.2013.64057.
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CASAGRANDE, ALBERTO, and FRANCESCO FABRIS. "FAMILY FINGERPRINTS: A GLOBAL APPROACH TO STRUCTURAL CLASSIFICATION." Journal of Bioinformatics and Computational Biology 10, no. 03 (June 2012): 1242001. http://dx.doi.org/10.1142/s0219720012420012.
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Protein domain classification is a useful tool to deduce functional properties of proteins. Many software to classify domains according to available databases have been proposed so far. This paper introduces the notion of "fingerprint" as an easy and readable digest of the similarities between a protein fragment and an entire set of sequences. This concept offers us a rationale for building an automatic SCOP classifier which assigns a query sequence to the most likely family. Fingerprint-based analysis has been implemented in a software tool and we report some experimental validations for it.
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Huang, Jiajie, Kejie Li, and Michael Gribskov. "Accurate Classification of RNA Structures Using Topological Fingerprints." PLOS ONE 11, no. 10 (October 18, 2016): e0164726. http://dx.doi.org/10.1371/journal.pone.0164726.
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Núñez, Nerea, Oscar Vidal-Casanella, Sonia Sentellas, Javier Saurina, and Oscar Núñez. "Non-Targeted Ultra-High Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UHPLC-HRMS) Fingerprints for the Chemometric Characterization and Classification of Turmeric and Curry Samples." Separations 7, no. 2 (June 10, 2020): 32. http://dx.doi.org/10.3390/separations7020032.
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In this work, non-targeted ultra-high performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) fingerprints obtained by C18 reversed-phase chromatography were proposed as sample chemical descriptors for the characterization and classification of turmeric and curry samples. A total of 21 turmeric and 9 curry commercially available samples were analyzed in triplicate after extraction with dimethyl sulfoxide (DMSO). The results demonstrated the feasibility of non-targeted UHPLC-HRMS fingerprints for sample classification, showing very good classification capabilities by partial least squares regression-discriminant analysis (PLS-DA), with 100% classification rates being obtained by PLS-DA when randomly selected samples were processed as “unknown” ones. Besides, turmeric curcuma species (Curcuma longa vs. Curcuma zedoaria) and turmeric Curcuma longa varieties (Madras, Erodes, and Alleppey) discrimination was also observed by PLS-DA when using the proposed fingerprints as chemical descriptors. As a conclusion, non-targeted UHPLC-HRMS fingerprinting is a suitable methodology for the characterization, classification, and authentication of turmeric and curry samples, without the requirement of using commercially available standards for quantification nor the necessity of metabolite identification.
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van Duijvenbode, Jeroen R., Mike W. N. Buxton, and Masoud Soleymani Shishvan. "Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework." Minerals 10, no. 4 (April 18, 2020): 366. http://dx.doi.org/10.3390/min10040366.
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Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material attributes could help to forecast the behaviour of mineral processing. This paper proposes a conceptual framework that creates a data-driven link between ore and the processing behaviour through the creation of material “fingerprints”. A fingerprint is a machine learning-based classification of measured material attributes compared to the range of attributes found within the mine’s mineral reserves. The outcome of the classification acts as a label for a machine learning model and contains relevant information, which may identify the root cause of measured differences in processing behaviour. Therefore, this class label can forecast the associated behaviour of mineral processing. Furthermore, insight is given into the confidence of available data originating from different analytical techniques. Taken together, this enhances the understanding of how differences in geology impact metallurgical plant performance. Targeted measurements at low-confidence unit processes and for specific attributes would upgrade the confidence in fingerprints and capabilities to predict plant performance.
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Guo, Yanyan, Xiangdong Fei, and Qijun Zhao. "Fingerprint Liveness Detection Using Multiple Static Features and Random Forests." International Journal of Image and Graphics 14, no. 04 (October 2014): 1450021. http://dx.doi.org/10.1142/s0219467814500211.
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It has been demonstrated that fingerprint recognition systems are susceptible to spoofing by presenting a well-duplicated synthetic such as a gummy finger. This paper proposes a novel software-based liveness detection approach using multiple static features. Given a fingerprint image, the static features, including fingerprint coarseness, first-order statistics and intensity-based features, are extracted. Unlike previous methods, the fingerprint coarseness is modeled as multiplicative noise rather than additive noise and is extracted by cepstral analysis. A random forest classifier is employed to select effective features among the extracted features and to differentiate fake from live fingerprints. The proposed method has been evaluated on the standard database provided in the Fingerprint Liveness Detection Competition 2009 (LivDet2009). Compared with other state-of-the-art methods, the proposed method reduces the average classification error rate by more than 20%.
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Barbosa, Sergio, Javier Saurina, Lluís Puignou, and Oscar Núñez. "Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)." Foods 9, no. 4 (April 13, 2020): 486. http://dx.doi.org/10.3390/foods9040486.
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In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.
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Zhou, Qingwei, Kewei Liu, Xiaolong Li, Yonghua Gu, Yuhong Zheng, Boyuan Fan, and Weihong Wu. "Voltammetric Electrochemical Sensor for Phylogenetic Study in Acer Linn." Biosensors 11, no. 9 (September 8, 2021): 323. http://dx.doi.org/10.3390/bios11090323.
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Acer Linn. is a highly divergent species morphology in the maple family (Aceraceae). It is one of the genera facing a very difficult taxonomic situation. The phylogeny of the genus and the taxonomic system under the genus remain unclear. The use of electrochemical fingerprints for plant phylogenetic study is an emerging application in biosensors. In this work, leaves of 18 species of Acer Linn. with an exo-taxa were selected for electrochemical fingerprint recording. Two different conditions were used for improving the data abundance. The fingerprint of all species showed a series of oxidation peaks. These peaks can be ascribed to the oxidation of flavonols, phenolic acids, procyanidins, alkaloids, and pigments in plant tissue. These electrochemical fingerprints can be used for the identification of plant species. We also performed a phylogenetic study with data from electrochemical fingerprinting. The phylogenetic tree of Acer is divided into three main clades. The result is in full agreement with A. shangszeense var. anfuense, A. pictum subsp. mono, A. amplum, A. truncatum, and A. miaotaiense, belonging to the subsection Platanoidea. A. nikoense and A. griseum were clustered together in the dendrogram. Another group that fits the traditional classification results is in the subsection Integrifolia.
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Hsiao, Chung-Ting, Chun-Yi Lin, Po-Shan Wang, and Yu-Te Wu. "Application of Convolutional Neural Network for Fingerprint-Based Prediction of Gender, Finger Position, and Height." Entropy 24, no. 4 (March 29, 2022): 475. http://dx.doi.org/10.3390/e24040475.
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Fingerprints are the most common personal identification feature and key evidence for crime scene investigators. The prediction of fingerprints features include gender, height range (tall or short), left or right hand, and finger position can effectively narrow down the list of suspects, increase the speed of comparison, and greatly improve the effectiveness of criminal investigations. In this study, we used three commonly used CNNs (VGG16, Inception-v3, and Resnet50) to perform biometric prediction on 1000 samples, and the results showed that VGG16 achieved the highest accuracy in identifying gender (79.2%), left- and right-hand fingerprints (94.4%), finger position (84.8%), and height range (69.8%, using the ring finger of male participants). In addition, we visualized the CNN classification basis by the Grad-CAM technique and compared the results with those predicted by experts and found that the CNN model outperformed experts in terms of classification accuracy and speed, and provided good reference for fingerprints that were difficult to determine manually.
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Oladimeji, Ismaila W., Omidiora E. Olusayo, Ismaila Folasade M., and Falohun Adeleye S.. "Multi-Level Access Control System in Automated Teller Machines." International Journal of Computer Science and Mobile Computing 10, no. 4 (April 30, 2021): 146–55. http://dx.doi.org/10.47760/ijcsmc.2021.v10i04.020.
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E-commerce theft involves using lost/stolen debit/credit cards, forging checks, misleading accounting practices, etc. Due to carelessness of cardholders and criminality activities of fraudsters, the personal identification number (PIN) and using account level based fraud detection techniques methods are inadequate to cub the activities of fraudsters. In recent times, researchers have made efforts of improving cyber-security by employing biometrics traits based security system for authentication. This paper proposed a multi-level fraud detection system in automated teller machine (ATM) operations. The system included PIN level, account-level and biometric level. Acquired RealScan-F scanner was used to capture liveness fingers. Transactional data were generated for each individual fingerprint with unique PIN. The results of the simulation showed that (i) the classification at account level only yielded averages 84.3% precision, 94.5% accuracy and 5.25% false alarm rate; (ii) matching at biometric level using liveness fingerprints samples yielded 0% APCER , 0% NPCER and 100% accuracy better than using fingerprints samples that produced 4.25% APCER , 2.33% NPCER and 93.42% accuracy; (iii) combining the three levels with the condition that all the levels must be positive produced 87.5% precision,84.9% accuracy and 2.65% false alarm rate; (iv) while the classification using voting technique yielded 99.15% precision, 97.35% accuracy and 0.47% false alarm.
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Cobzac, Simona Codruța Aurora, Neli Kinga Olah, and Dorina Casoni. "Application of HPTLC Multiwavelength Imaging and Color Scale Fingerprinting Approach Combined with Multivariate Chemometric Methods for Medicinal Plant Clustering According to Their Species." Molecules 26, no. 23 (November 29, 2021): 7225. http://dx.doi.org/10.3390/molecules26237225.
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In the current study, multiwavelength detection combined with color scales HPTLC fingerprinting procedure and chemometric approach were applied for direct clustering of a set of medicinal plants with different geographical growing areas. The fingerprints profiles of the hydroalcoholic extracts obtained after single and double development and detection under 254 nm and 365 nm, before and after selective spraying with specific derivatization reagents were evaluated by chemometric approaches. Principal component analysis (PCA) with factor analysis (FA) methods were used to reveal the contribution of red (R), green (G), blue (B) and, respectively, gray (K) color scale fingerprints to HPTLC classification of the analyzed samples. Hierarchical cluster analysis (HCA) was used to classify the medicinal plants based on measure of similarity of color scale fingerprint patterns. The 1-Pearson distance measurement with Ward’s amalgamation procedure proved to be the most convenient approach for the correct clustering of samples. Data from color scale fingerprints obtained for double development procedure and multiple visualization modes combined with appropriate chemometric methods proved to detect the similar medicinal plant extracts even though they are from different geographical regions, have different storage conditions and no specific markers are individually extracted. This approach could be proposed as a promising tool for authentication and identification studies of plant materials based on HPTLC fingerprinting analysis.
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Xu, Lu, Qiong Shi, Si-Min Yan, Hai-Yan Fu, Shunping Xie, and Daowang Lu. "Chemometric Analysis of Elemental Fingerprints for GE Authentication of Multiple Geographical Origins." Journal of Analytical Methods in Chemistry 2019 (July 11, 2019): 1–7. http://dx.doi.org/10.1155/2019/2796502.
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The feasibility of combining elemental fingerprints and chemical pattern recognition methods for authentication of the geographical origins of a Chinese herb, Gastrodia elata BI. (GE), was studied in this paper. A total of 210 GE samples were collected from 7 different producing areas. The levels of 15 mineral elements in GE, including Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K, were determined using inductively coupled plasma mass spectrometry (ICP-MS). Using the autoscaled data of elemental fingerprints and partial least-squares discriminant analysis (PLSDA), two chemometrics strategies for multiclass classifications, One-Versus-Rest (OVR) and One-Versus-One (OVO), were studied and compared in discrimination of GE geographical origins. As a result, OVR-PLSDA and OVO-PLSDA could achieve the classification accuracy of 0.672 and 0.925, respectively. The results indicate that mineral elemental fingerprints coupled with chemometrics can provide a useful alternative method for simultaneous discrimination of multiple GE geographical origins.
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Shen, Yu, and Wang. "Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics." Molecules 24, no. 14 (July 14, 2019): 2562. http://dx.doi.org/10.3390/molecules24142562.
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Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies (“low-level” and “mid-level”). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.
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Dincă Lăzărescu, Andreea-Monica, Simona Moldovanu, and Luminita Moraru. "A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks." Inventions 7, no. 2 (May 27, 2022): 39. http://dx.doi.org/10.3390/inventions7020039.
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This study presents an algorithm for fingerprint classification using a CNN (convolutional neural network) model and making use of full images belonging to four digital databases. The main challenge that we face in fingerprint classification is dealing with the low quality of fingerprints, which can impede the identification process. To overcome these restrictions, the proposed model consists of the following steps: a preprocessing stage which deals with edge enhancement operations, data resizing, data augmentation, and finally a post-processing stage devoted to classification tasks. Primarily, the fingerprint images are enhanced using Prewitt and Laplacian of Gaussian filters. This investigation used the fingerprint verification competition with four databases (FVC2004, DB1, DB2, DB3, and DB4) which contain 240 real fingerprint images and 80 synthetic fingerprint images. The real images were collected using various sensors. The innovation of the model is in the manner in which the number of epochs is selected, which improves the performance of the classification. The number of epochs is defined as a hyper-parameter which can influence the performance of the deep learning model. The number of epochs was set to 10, 20, 30, and 50 in order to keep the training time at an acceptable value of 1.8 s/epoch, on average. Our results indicate the overfitting of the model starting with the seventh epoch. The accuracy varies from 67.6% to 98.7% for the validation set, and between 70.2% and 75.6% for the test set. The proposed method achieved a very good performance compared to the traditional hand-crafted features despite the fact that it used raw data and it does not perform any handcrafted feature extraction operations.
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Zhang, Yuewen, Maya A. Wright, Kadi L. Saar, Pavankumar Challa, Alexey S. Morgunov, Quentin A. E. Peter, Sean Devenish, Christopher M. Dobson, and Tuomas P. J. Knowles. "Machine learning-aided protein identification from multidimensional signatures." Lab on a Chip 21, no. 15 (2021): 2922–31. http://dx.doi.org/10.1039/d0lc01148g.
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Nguyen, Huong Thu, and Long The Nguyen. "Fingerprints Classification through Image Analysis and Machine Learning Method." Algorithms 12, no. 11 (November 11, 2019): 241. http://dx.doi.org/10.3390/a12110241.
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The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%) 2 / 3 databases.
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Jaimes-Díaz, Hueman, Adda Jeanette García-Chéquer, Alfonso Méndez-Tenorio, Juan Carlos Santiago-Hernández, Rogelio Maldonado-Rodríguez, and Kenneth Loren Beattie. "Bacterial classification using genomic fingerprints obtained by virtual hybridization." Journal of Microbiological Methods 87, no. 3 (December 2011): 286–94. http://dx.doi.org/10.1016/j.mimet.2011.08.014.
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Gutiérrez-Gómez, Leonardo, and Jean-Charles Delvenne. "Multi-hop assortativities for network classification." Journal of Complex Networks 7, no. 4 (December 18, 2018): 603–22. http://dx.doi.org/10.1093/comnet/cny034.
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Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.