Relevant bibliographies by topics / Fingerprints Classification

Academic literature on the topic 'Fingerprints Classification'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Fingerprints Classification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Fingerprints Classification"

1

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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%.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
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.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
<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>
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Fingerprints Classification"

1

Massimiliani, Lorenzo. "Classification and clustering of video fingerprints: preliminary results." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22973/.

Full text
Abstract:
Many photos and videos are produced and uploaded to the Internet every day. Even though this is a small part of the total, a large amount of them is illegal. Once digital content has been distributed online, it is often difficult to re-associate the photo or video to the device that produced it or to the user who initially shared it. To counter the spread of illegal content, there is a branch of studies called “source camera identification”, which aims to reconnect a photo or video to the device that developed it. The idea behind source camera identification is that each camera, having imperfections that make it unique, gives a digital fingerprint to the content it produces. The noise of a digital content, which represents a variation of intensity that cannot be found in the recorded content, contains the fingerprint along with some random factors. The noises, which are extracted through denoising algorithms, can be used directly to identify the device that produced the content, or they can be used to estimate the fingerprint. This thesis works in the source camera identification of video content. Two datasets are considered: one called Vision, which is considered the reference dataset in this area and one made available by the University of Bologna. The work carried out in this thesis was to extract the noises on those datasets, and calculate the fingerprints, comparing different approaches present in the state of the art. The approach that was chosen has yielded the best results through a classi- fication algorithm. Once the noises were extracted and the fingerprints calculated, classification and clustering techniques were applied. Two classification techniques have been developed one through convolutional neural network and another using a function called Peak-to-correlation energy. Clustering algorithms have been applied, already developed to work in this area, one that considers a known number of cameras and another that considers an unknown number.
APA, Harvard, Vancouver, ISO, and other styles
2

Sutanto, Kevin. "RNA Sequence Classification Using Secondary Structure Fingerprints, Sequence-Based Features, and Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41876.

Full text
Abstract:
RNAs are involved in different facets of biological processes; including but not limited to controlling and inhibiting gene expressions, enabling transcription and translation from DNA to proteins, in processes involving diseases such as cancer, and virus-host interactions. As such, there are useful applications that may arise from studies and analyses involving RNAs, such as detecting cancer by measuring the abundance of specific RNAs, detecting and identifying infections involving RNA viruses, identifying the origins of and relationships between RNA viruses, and identifying potential targets when designing novel drugs. Extracting sequences from RNA samples is usually not a major limitation anymore thanks to sequencing technologies such as RNA-Seq. However, accurately identifying and analyzing the extracted sequences is often still the bottleneck when it comes to developing RNA-based applications. Like proteins, functional RNAs are able to fold into complex structures in order to perform specific functions throughout their lifecycle. This suggests that structural information can be used to identify or classify RNA sequences, in addition to the sequence information of the RNA itself. Furthermore, a strand of RNA may have more than one possible structural conformations it can fold into, and it is also possible for a strand to form different structures in vivo and in vitro. However, past studies that utilized secondary structure information for RNA identification purposes have relied on one predicted secondary structure for each RNA sequence, despite the possible one-to-many relationship between a strand of RNA and the possible secondary structures. Therefore, we hypothesized that using a representation that includes the multiple possible secondary structures of an RNA for classification purposes may improve the classification performance. We proposed and built a pipeline that produces secondary structure fingerprints given a sequence of RNA, that takes into account the aforementioned multiple possible secondary structures for a single RNA. Using this pipeline, we explored and developed different types of secondary structure fingerprints in our studies. A type of fingerprints serves as high-level topological representations of the RNA structure, while another type represents matches with common known RNA secondary structure motifs we have curated from databases and the literature. Next, to test our hypothesis, the different fingerprints are then used with deep learning and with different datasets, alone and together with various sequence-based features, to investigate how the secondary structure fingerprints affect the classification performance. Finally, by analyzing our findings, we also propose approaches that can be adopted by future studies to further improve our secondary structure fingerprints and classification performance.
APA, Harvard, Vancouver, ISO, and other styles
3

Späth, Bastian, Matthias Philipp, and Thomas Bartnitzki. "Machine performance and acoustic fingerprints of cutting and drilling." TU Bergakademie Freiberg, 2017. https://tubaf.qucosa.de/id/qucosa%3A23182.

Full text
Abstract:
‘It is always dark ahead of the pick!’ This centuries-old miners’ expression still reveals the uncertainty about the upcoming rock properties during exploration and extraction processes. It is still tough to predict what a drill rig or a cutting machine will experience during operation. However, in terms of safety, energy consumption and the performance of the whole machine it would be beneficial to be able to monitor such an extraction process. Hence, different sensors or sensor combinations are tested during cutting and drilling processes within RealTime Mining project. First aim is to depict the machine performance of the machine at any time. In a second step sensor information is also used to conclude on mechanical rock properties during the process. Measuring the machine performance for cutting and drilling is quite similar and has been condensed under the terms Monitoring-While-Cutting (MWC) respectively Monitoring-While-Drilling (MWD). Both monitoring systems contain a bundle of sensors to depict the whole process. As an example, the energy demand of such a machine can be determined by measuring the power consumption of the engines constantly. Furthermore, the process parameters like advance rates and drilling or cutting speed have to be evaluated as well to be able to depict the whole extraction machine. To conclude on mechanical rock properties several other sensor solutions have been tested and finally integrated into those monitoring systems. One of the most important rock properties for drilling and cutting is the rock strength. Increasing rock strength during an extraction process leads to increasing forces that are needed to break a certain amount of rock. Hence, e.g. measuring the torque of a drill string or the cutting forces can be an indicator on rock resistance or rock strength. Not minor important, is the characteristic rock breakage behavior which can be classified by the use of ‘acoustic’ sensors. Dependent on the rock properties that currently is drilled or cut through a characteristic fracture occurs in front of the tool. This results in audible and also inaudible characteristic acoustic waves that propagate through the machine body and can be gathered on the machine by piezo-electric sensors. The interpretation of these signals could lead to a material classification already during the extraction process. Several tests of these sensor technologies have been conducted in laboratory environment as well in field tests. The most promising results are going to be presented.
APA, Harvard, Vancouver, ISO, and other styles
4

Tian, Ye. "Développement d'une méthode de géolocalisation à l'intérieur de bâtiments par classification des fingerprints GSM et fusion de données de capteurs embarqués." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066027/document.

Full text
Abstract:
L’objet de cette thèse est l’étude de la localisation et de la navigation à l’intérieur de bâtiments à l’aide des signaux disponibles dans les systèmes mobiles cellulaires et, en particulier, les signaux GSM.Le système GPS est aujourd’hui couramment utilisé en extérieur pour déterminer la position d’un objet, mais les signaux GPS ne sont pas adaptés à la localisation en intérieurIci, la localisation en intérieur est obtenue à partir de la technique des «empreintes» de puissance des signaux reçus sur les canaux utilisés par les réseaux GSM. Elle est réalisée à l’échelle de la pièce. Une classification est effectuée à partir de machines à vecteurs supports et les descripteurs utilisés sont les puissances de toutes les porteuses GSM. D’autres capteurs physiques disponibles dans les téléphones portables fournissent des informations utiles pour déterminer la position ou le déplacement de l’utilisateur. Celles-ci, ainsi que la cartographie de l’environnement, sont associées aux résultats obtenus à partir des «empreintes» GSM au sein de filtres particulaires afin d’obtenir une localisation plus précise, et sous forme de coordonnées continues.Les résultats obtenus montrent que l’utilisation des seules empreintes GSM permet de déterminer la pièce correcte dans 94% des cas sur une durée courte et que les performances restent stables pendant plusieurs mois, de l’ordre de 80%, si les données d’apprentissage sont enregistrées sur quelques jours. L’association de la cartographie du lieu et des informations issues des autres capteurs aux données de classification permettent d’obtenir les coordonnées de la trajectoire du système mobile avec une bonne précision et une bonne fiabilité
GPS has long been used for accurate and reliable outdoor localization, but it cannot operate in indoor environments, which suggests developing indoor localization methods that can provide seamless and ubiquitous services for mobile users.In this thesis, indoor localization is realized making use of received signal strength fingerprinting technique based on the existing GSM networks. A room is defined as the minimum location unit, and support vector machine are used as a mean to discriminate the rooms by classifying received signal strengths from very large number of GSM carriers. At the same time, multiple sensors, such as accelerometer and gyroscope, are widely available for modern mobile devices, which provide additional information that helps location determination. The hybrid approach that combines the GSM fingerprinting results with mobile sensor and building layout information using a particle filter provides a more accurate and fine-grained localization result.The results of experiments under realistic conditions demonstrate that correct room number can be obtained 94% of the time provided the derived model is used before significant received signal strength drift sets in. Furthermore, if the training data is sampled over a few days, the performance can remain stable exceeding 80% over a period of months, and can be further improved with various post-processing techniques. Moreover, including the mobile sensors allows the system to localize the mobile trajectory coordinates with high accuracy and reliability
APA, Harvard, Vancouver, ISO, and other styles
5

Zhang, Fan. "Fingerprint classification with combined neural networks." Thesis, University of Surrey, 2009. http://epubs.surrey.ac.uk/2216/.

Full text
Abstract:
Biometric identification has been widely used in identifying a genuine person from an impostor. Fingerprint identification is becoming a very popular biometric identification technique because it has special properties: fingerprints are unique and unchangeable. With increased processing capability of computers and larger the size of fingerprint databases are increased, the demand for higher speed processing and greater processing capacity for automatic fingerprint identification systems (AFIS) has increased. APIS consists of fingerprint feature acquisition, fingerprint classification and fingerprint matching. Fingerprint classification plays a key role in fingerprint identification as efficient and accurate algorithms cannot only reduce the search time for searching large fingerprint databases, but they can also reduce the number of fingerprints that need to be searched.
APA, Harvard, Vancouver, ISO, and other styles
6

Mohamed, Suliman M. "Fingerprint-based biometric recognition allied to fuzzy-neural feature classification." Thesis, Sheffield Hallam University, 2002. http://shura.shu.ac.uk/20071/.

Full text
Abstract:
The research investigates fingerprint recognition as one of the most reliable biometrics identification methods. An automatic identification process of humans-based on fingerprints requires the input fingerprint to be matched with a large number of fingerprints in a database. To reduce the search time and computational complexity, it is desirable to classify the database of fingerprints into an accurate and consistent manner so that the input fingerprint is matched only with a subset of the fingerprints in the database. In this regard, the research addressed fingerprint classification. The goal is to improve the accuracy and speed up of existing automatic fingerprint identification algorithms. The investigation is based on analysis of fingerprint characteristics and feature classification using neural network and fuzzy-neural classifiers. The methodology developed, is comprised of image processing, computation of a directional field image, singular-point detection, and feature vector encoding. The statistical distribution of feature vectors was analysed using SPSS. Three types of classifiers, namely, multi-layered perceptrons, radial basis function and fuzzy-neural methods were implemented. The developed classification systems were tested and evaluated on 4,000 fingerprint images on the NIST-4 database. For the five-class problem, classification accuracy of 96.2% for FNN, 96.07% for MLP and 84.54% for RBF was achieved, without any rejection. FNN and MLP classification results are significant in comparison with existing studies, which have been reviewed.
APA, Harvard, Vancouver, ISO, and other styles
7

Kim, Dae Wook. "Data-Driven Network-Centric Threat Assessment." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495191891086814.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wang, Yi, and alice yi wang@gmail com. "Ridge Orientation Modeling and Feature Analysis for Fingerprint Identification." RMIT University. Computer Science and Information Technology, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091009.152317.

Full text
Abstract:
This thesis systematically derives an innovative approach, called FOMFE, for fingerprint ridge orientation modeling based on 2D Fourier expansions, and explores possible applications of FOMFE to various aspects of a fingerprint identification system. Compared with existing proposals, FOMFE does not require prior knowledge of the landmark singular points (SP) at any stage of the modeling process. This salient feature makes it immune from false SP detections and robust in terms of modeling ridge topology patterns from different typological classes. The thesis provides the motivation of this work, thoroughly reviews the relevant literature, and carefully lays out the theoretical basis of the proposed modeling approach. This is followed by a detailed exposition of how FOMFE can benefit fingerprint feature analysis including ridge orientation estimation, singularity analysis, global feature characterization for a wide variety of fingerprint categories, and partial fin gerprint identification. The proposed methods are based on the insightful use of theory from areas such as Fourier analysis of nonlinear dynamic systems, analytical operators from differential calculus in vector fields, and fluid dynamics. The thesis has conducted extensive experimental evaluation of the proposed methods on benchmark data sets, and drawn conclusions about strengths and limitations of these new techniques in comparison with state-of-the-art approaches. FOMFE and the resulting model-based methods can significantly improve the computational efficiency and reliability of fingerprint identification systems, which is important for indexing and matching fingerprints at a large scale.
APA, Harvard, Vancouver, ISO, and other styles
9

Wescher, Agnes. "Molekularbiologische Typisierung von Streptococcus canis isoliert aus subklinisch mastitiskranken Kühen in hessischen Milchviehbetrieben." Doctoral thesis, Universitätsbibliothek Leipzig, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:15-20090609-095913-0.

Full text
Abstract:
In der vorliegenden Arbeit wurden 2460 Viertelgemelksproben aus 16 hessischen Milcherzeugerbetrieben untersucht. 115 S. canis-Isolate konnten gefunden und auf ihre morphologischen, biochemischen und bei molekularbiologischen Eigenschaften untersucht werden. Die Isolate stammten von Viertelgemelksproben bzw. Tankproben, die zu einem oder mehreren Zeitpunkten in den Betrieben genommen wurden. Die Untersuchung der biochemischen Eigenschaften erbrachte 24 verschiedene Reaktionsmuster. Der Vergleich dieser 24 Biotypen mit einem S. canis-Referenzstamm mittels tDNA-PCR und 16S-RNA-PCR ergab eine völlige Übereinstimmung (100%) und damit eine sichere Spezies-Identifizierung. Zur Aufklärung epidemiologischer Zusammenhänge und zur Intra-Spezies-Identifizierung wurde von allen 115 Isolaten mittels PFGE nach Makrorestriktionsverdau mit SmaI ein DNA-Fingerprint erstellt. Dabei ergaben sich 21 verschiedene Restriktionsmuster. Von den 21 nach Makrorestriktion mit Sma I und anschließender PFGE unterscheidbaren Restriktionsmustern wurde je ein Isolat zur Bestimmung der Differenzierungsfähigkeit der Restriktionsenzyme Cla I und Apa I sowie der RAPD-PCR weitergehend untersucht. Für die Beurteilung epidemiologischer Zusammenhänge bei S. canis erwies sich die PFGE nach Makrorestriktion mittels Sma I als die differenzierteste Variante. Die mittels PFGE nach Makrorestriktionsverdau mit Sma I durchgeführten Untersuchungen der 115 Isolate zeigten, dass zu einem Probennahme-Termin gewonnene Isolate identisch waren; vom gleichen Betrieb zu unterschiedlichen Zeiten entnommene Proben zeigten z.T. deutliche Unterschiede, und bei Isolaten von verschiedenen Betrieben konnten keine Verwandtschaftsbeziehungen nachgewiesen werden. Aufgrund dieser genotypischen Eigenschaften der Kulturen konnte gezeigt werden, dass es sich bei durch S. canis verursachte Mastitiden um ein infektiöses Bestandsproblem handelt, bei dem der Erreger von Viertel zu Viertel und von Kuh zu Kuh übertragen wird.
APA, Harvard, Vancouver, ISO, and other styles
10

Eschenhagen, Martin. "Molekulare Untersuchung zweier Belebtschlammanlagen unter besonderer Berücksichtigung der biologischen Phosphorelimination." Doctoral thesis, Technische Universität Dresden, 2003. https://tud.qucosa.de/id/qucosa%3A24360.

Full text
Abstract:
Aufgrund der ökologischen und ökonomischen Problematik der chemischen Phosphatfällung ist eine Optimierung der Effizienz und Stabilität der biologischen Verfahren zur Phosphat-elimination erforderlich. Hierfür ist jedoch ein fundiertes Wissen über die daran beteiligten Organismen eine entscheidende Vorraussetzung. Das Ziel der vorliegenden Arbeit war es, die mikrobielle Populationstruktur von zwei Belebtschlamm-anlagen im Labormaßstab mit Hilfe von drei unterschiedlichen 16S rDNA basierenden molekular-biologischen Methoden zu charakterisieren. Ein besonderer Schwer-punkt ist hierbei die Analyse der Bakterien, die mit der erhöhten biologischen Phosphat-elimination in Verbindung gebracht werden. Dies sind Vertreter der Rhodocyclus-Gruppe, der Gattung Tetrasphaera und der Gattung Acinetobacter. Als Untersuchungsobjekte wurden zwei Hauptstromverfahren zur erhöhten biologischen Phosphatelimination gewählt, die sich im Schlamm-alter, der Schlammbelastung und der sich daraus resultierenden Nitrifikationsleistung unterscheiden. Aufgrund der gewählten Verfahrensweisen wurde der Einfluss der Nitrifikation auf die Zusammensetzung der Belebtschlammbiozönose ebenfalls untersucht. Um praxisnahe Verhältnisse zu erreichen, wurden die Anlagen mit kommunalem Abwasser beschickt. Für einen Vergleich sollten Proben aus kommunalen Kläranlagen mit deutlich anderen Verfahrensweisen in die Untersuchungen mit einbezogen werden.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Fingerprints Classification"

1

Galton, Francis. Fingerprints. Buffalo, N.Y: W.S. Hein, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hawthorne, Mark R. Fingerprints: Analysis and understanding. Boca Raton: CRC Press, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Fingerprints: Innocence or guilt : the identity factors. Chicago, Ill: Terk Books and Publishers, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bowen, Jacqueline D. Automatic fingerprint pattern classification using neutral networks. London: Home Office, Science and Technology Group, 1992.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Maltoni, Davide. Handbook of fingerprint recognition. New York: Springer, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ahouse, Jeremy John. Fingerprinting. Berkeley, Calif: Great Explorations in Math and Science (GEMS), Lawrence Hall of Science, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ahouse, Jeremy John. Fingerprinting: Teacher's guide. Berkeley, CA: Lawrence Hall of Science, University of California, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Dario, Maio, Jain Anil K, Prabhakar Salil, and SpringerLink (Online service), eds. Handbook of Fingerprint Recognition. London: Springer London, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hawthorne, Mark R., Sharon Plotkin, and Bracey-Ann Francis Douglas. Fingerprints. Taylor & Francis Group, 2021.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hawthorne, Mark R., Sharon Plotkin, and Bracey-Ann Douglas. Fingerprints. Taylor & Francis Group, 2021.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Fingerprints Classification"

1

Sousedik, Ctirad, Ralph Breithaupt, and Patrick Bours. "Classification of Fingerprints Captured Using Optical Coherence Tomography." In Image Analysis, 326–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59129-2_28.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Martin, G. R., P. Leff, and S. J. Maclennan. "Tryptamine fingerprints in the classification of 5-hydroxytryptamine receptors." In Cardiovascular Pharmacology of 5-Hydroxytryptamine, 157–62. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0479-8_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Aston, Tracy-ann. "Classification and identification: How can fingerprints solve a crime?" In The Really Useful Book Of Secondary Science Experiments, 46–47. Abingdon, Oxon ; New York, NY : Routledge, [2017]: Routledge, 2017. http://dx.doi.org/10.4324/9781315640082-23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Josphineleela, R., and M. Ramakrishnan. "A New Classification Algorithm with GLCCM for the Altered Fingerprints." In Information Technology and Mobile Communication, 352–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20573-6_62.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tang, Zixian, Qiang Wang, Wenhao Li, Huaifeng Bao, Feng Liu, and Wen Wang. "HSLF: HTTP Header Sequence Based LSH Fingerprints for Application Traffic Classification." In Computational Science – ICCS 2021, 41–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77961-0_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mishra, Annapurna, Satchidananda Dehuri, and Pradeep Kumar Mallick. "Feature Extraction and Feature Sheet Preparation of Real-Time Fingerprints for Classification Application." In Lecture Notes in Electrical Engineering, 71–83. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9488-2_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Stead, D. E., S. A. Simpkins, S. A. Weller, J. Hennessy, A. Aspin, H. Stanford, N. C. Smith, and J. G. Elphinstone. "Classification and Identification of Plant Pathogenic Pseudomonas species by REP-PCR Derived Genetic Fingerprints." In Pseudomonas syringae and related pathogens, 411–20. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0133-4_45.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Woźnica, Adam, Alexandros Kalousis, and Melanie Hilario. "Kernels on Lists and Sets over Relational Algebra: An Application to Classification of Protein Fingerprints." In Advances in Knowledge Discovery and Data Mining, 546–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_64.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Rahman, S. M. Mahbubur, Tamanna Howlader, and Dimitrios Hatzinakos. "Fingerprint Classification." In Orthogonal Image Moments for Human-Centric Visual Pattern Recognition, 117–28. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9945-0_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jiang, Xudong. "Fingerprint Classification." In Encyclopedia of Biometrics, 439–46. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_56.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Fingerprints Classification"

1

Li, Jun, Wei-Yun Yau, and Han Wang. "Continuous fingerprints classification by symmetrical filters." In the 2006 ACM Symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1128817.1128872.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

"Gender Classification based on Fingerprints using SVM." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004721602410244.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ohta, Jun, John P. Sharpe, and Kristina M. Johnson. "Optoelectronic feature extractor for classification of fingerprints." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Joseph L. Horner, Bahram Javidi, Stephen T. Kowel, and William J. Miceli. SPIE, 1993. http://dx.doi.org/10.1117/12.163589.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Xiangrong, Guohui Wang, and Xiangjiang Lu. "Neural Network Based Automatic Fingerprints Classification Algorithm." In 2010 International Conference of Information Science and Management Engineering. ISME 2010. IEEE, 2010. http://dx.doi.org/10.1109/isme.2010.187.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Ma, Runcong, Gwo-Jong Yu, Guilin Chen, Shenghui Zhao, and Bin Yang. "Hierarchical CSI-fingerprints Classification for Passive Multi-person Localization." In 2017 International Conference on Networking and Network Applications (NaNA). IEEE, 2017. http://dx.doi.org/10.1109/nana.2017.56.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Basak, Sanjoy, Sreeraj Rajendran, Sofie Pollin, and Bart Scheers. "Drone classification from RF fingerprints using deep residual nets." In 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2021. http://dx.doi.org/10.1109/comsnets51098.2021.9352891.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hamdi, Dhekra El, Ines Elouedi, Abir Fathallah, Mai K. Nguyuen, and Atef Hamouda. "Combining Fingerprints and their Radon Transform as Input to Deep Learning for a Fingerprint Classification Task." In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018. http://dx.doi.org/10.1109/icarcv.2018.8581072.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Conti, Vincenzo, Carmelo Militello, Salvatore Vitabile, and Filippo Sorbello. "Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition." In 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). IEEE, 2010. http://dx.doi.org/10.1109/cisis.2010.134.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Novikov, Sergey O., and Valery S. Kot. "Singular feature detection and classification of fingerprints using Hough transform." In Sixth International Workshop on Digital Image Processing and Computer Graphics, edited by Emanuel Wenger and Leonid I. Dimitrov. SPIE, 1998. http://dx.doi.org/10.1117/12.301375.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Webb, Leandra, and Mmamolatelo Mathekga. "Towards a Complete Rule-Based Classification Approach for Flat Fingerprints." In 2014 Second International Symposium on Computing and Networking (CANDAR). IEEE, 2014. http://dx.doi.org/10.1109/candar.2014.80.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Fingerprints Classification"

1

Candela, G. T., P. J. Grother, C. I. Watson, R. A. Wilkinson, and C. L. Wilson. PCASYS - a pattern-level classification automation system for fingerprints. Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5647.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Wilson, C. L., G. Candela, P. J. Grother, C. I. Watson, and R. A. Wilkinson. Massively parallel neural network fingerprint classification system. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4880.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Grother, P. J. Comparison of FFT fingerprint filtering methods for neural network classification. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5493.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wilson, Charles L., James L. Blue, and Omid M. Omidvar. Improving neural network performance for character and fingerprint classification by altering network dynamics. Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5695.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Comparative performance of classification methods for fingerprints. Gaithersburg, MD: National Institute of Standards and Technology, 1993. http://dx.doi.org/10.6028/nist.ir.5163.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Fingerprints Classification' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography