Relevant bibliographies by topics / Fingerprint Liveness Detection

Academic literature on the topic 'Fingerprint Liveness Detection'

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Published: 14 March 2023

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Journal articles on the topic "Fingerprint Liveness Detection"

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Jiang, Yujia, and Xin Liu. "Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid." Journal of Electrical and Computer Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1539298.

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Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.
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NIKAM, SHANKAR BHAUSAHEB, and SUNEETA AGARWAL. "CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION." International Journal of Image and Graphics 09, no. 02 (April 2009): 171–99. http://dx.doi.org/10.1142/s0219467809003393.

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Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.
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Lee, Youn Kyu, Jongwook Jeong, and Dongwoo Kang. "An Effective Orchestration for Fingerprint Presentation Attack Detection." Electronics 11, no. 16 (August 11, 2022): 2515. http://dx.doi.org/10.3390/electronics11162515.

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Fingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a number of techniques have focused on fingerprint presentation attack detection, they suffer from inaccuracy in determining the liveness of fingerprints and performance degradation on unknown types of fingerprints. To address existing limitations, we present a robust fingerprint presentation attack detection method that orchestrates different types of neural networks by incorporating a triangular normalization method. Our method has been evaluated on a public benchmark comprising 13,000 images with five different fake materials. The evaluation exhibited our method’s higher accuracy in determining the liveness of fingerprints as well as better generalization performance on different types of fingerprints compared to existing techniques.
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Babikir Adam, Edriss Eisa, and Sathesh. "Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View." Journal of ISMAC 3, no. 1 (March 1, 2021): 16–30. http://dx.doi.org/10.36548/jismac.2021.1.002.

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Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.
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Almehmadi, Abdulaziz. "A Behavioral-Based Fingerprint Liveness and Willingness Detection System." Applied Sciences 12, no. 22 (November 11, 2022): 11460. http://dx.doi.org/10.3390/app122211460.

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Fingerprints have been used for decades to verify the identity of an individual for various security reasons. Attackers have developed many approaches to deceive a fingerprint verification system, ranging from the sensor level, where gummy fingers are created, to gaining access to the decision-maker level, where the decision is made based on low matching criteria. Even though fingerprint sensor-level countermeasures have developed advanced metrics to detect any attempt to dupe the system, attackers still manage to outwit a fingerprint verification system. In this paper, we present the Micro-behavioral Fingerprint Analysis System (MFAS), a system that records the micro-behavior of the user’s fingertips over time as they are placing their fingerprint on the sensor. The system captures the stream of ridges as they are formed while placed on a sensor to combat the attacks that deceive the sensor. An experiment on 24 people was conducted, wherein the fingerprints and the behavior of the fingertip as it is placed were collected. Subsequently, a gummy finger was created to try to fool the system. Further, a legitimate user was chosen to participate in an experiment that mimicked an attempt to use their fingertip unwillingly to detect coerced fingerprint placement. After applying the micro-behavior, the system reported 100% true positives and 0% false-negatives when providing legitimate vs. gummy-based fingerprints to authenticate a malicious user. The system also reported a 100% accuracy in differentiating between a voluntary and a coerced fingerprint placement. The results improve the fingerprint robustness against attacks on a fingerprint sensor by factoring in micro-behavior, thus helping to overcome fake and coerced fingerprint attacks.
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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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F.W. Onifade, Olufade, Paul Akinde, and Folasade Olubusola Isinkaye. "Circular Gabor wavelet algorithm for fingerprint liveness detection." Journal of Advanced Computer Science & Technology 9, no. 1 (January 11, 2020): 1. http://dx.doi.org/10.14419/jacst.v9i1.29908.

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Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.
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Moon, Y. S., J. S. Chen, K. C. Chan, K. So, and K. C. Woo. "Wavelet based fingerprint liveness detection." Electronics Letters 41, no. 20 (2005): 1112. http://dx.doi.org/10.1049/el:20052577.

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Rani, Rajneesh, and Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach." International Journal of Intelligent Information Technologies 17, no. 1 (January 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.

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In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).
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Drahansky, Martin, Michal Dolezel, Jan Vana, Eva Brezinova, Jaegeol Yim, and Kyubark Shim. "New Optical Methods for Liveness Detection on Fingers." BioMed Research International 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/197925.

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This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities—the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection.
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Dissertations / Theses on the topic "Fingerprint Liveness Detection"

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Sandström, Marie. "Liveness Detection in Fingerprint Recognition Systems." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2397.

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Biometrics deals with identifying individuals with help of their biological data. Fingerprint scanning is the most common method of the biometric methods available today. The security of fingerprint scanners has however been questioned and previous studies have shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint recognition systems are evolving and this study will discuss the situation of today.

Two approaches have been used to find out how good fingerprint recognition systems are in distinguishing between live fingers and artificial clones. The first approach is a literature study, while the second consists of experiments.

A literature study of liveness detection in fingerprint recognition systems has been performed. A description of different liveness detection methods is presented and discussed. Methods requiring extra hardware use temperature, pulse, blood pressure, electric resistance, etc., and methods using already existent information in the system use skin deformation, pores, perspiration, etc.

The experiments focus on making artificial fingerprints in gelatin from a latent fingerprint. Nine different systems were tested at the CeBIT trade fair in Germany and all were deceived. Three other different systems were put up against more extensive tests with three different subjects. All systems werecircumvented with all subjects'artificial fingerprints, but with varying results. The results are analyzed and discussed, partly with help of the A/R value defined in this report.

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GHIANI, LUCA. "Textural features for fingerprint liveness detection." Doctoral thesis, Università degli Studi di Cagliari, 2015. http://hdl.handle.net/11584/266594.

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The main topic ofmy research during these three years concerned biometrics and in particular the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. Several videos posted on YouTube show how to violate these devices by using fake fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a threat to the existing fingerprint recognition systems. Despite the fact that many algorithms have been proposed so far, none of them showed the ability to clearly discriminate between real and fake fingertips. In my work, after a study of the state-of-the-art I paid a special attention on the so called textural algorithms. I first used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms in the FLD field. In the last two years I worked especially on what we called the “user specific” problem. In the extracted features we noticed the presence of characteristic related not only to the liveness but also to the different users. We have been able to improve the obtained results identifying and removing, at least partially, this user specific characteristic. Since 2009 the Department of Electrical and Electronic Engineering of the University of Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity have organized the Fingerprint Liveness Detection Competition (LivDet). I have been involved in the organization of both second and third editions of the Fingerprint Liveness Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets.
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Memon, Shahzad Ahmed. "Novel active sweat pores based liveness detection techniques for fingerprint biometrics." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7060.

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Liveness detection in automatic fingerprint identification systems (AFIS) is an issue which still prevents its use in many unsupervised security applications. In the last decade, various hardware and software solutions for the detection of liveness from fingerprints have been proposed by academic research groups. However, the proposed methods have not yet been practically implemented with existing AFIS. A large amount of research is needed before commercial AFIS can be implemented. In this research, novel active pore based liveness detection methods were proposed for AFIS. These novel methods are based on the detection of active pores on fingertip ridges, and the measurement of ionic activity in the sweat fluid that appears at the openings of active pores. The literature is critically reviewed in terms of liveness detection issues. Existing fingerprint technology, and hardware and software solutions proposed for liveness detection are also examined. A comparative study has been completed on the commercially and specifically collected fingerprint databases, and it was concluded that images in these datasets do not contained any visible evidence of liveness. They were used to test various algorithms developed for liveness detection; however, to implement proper liveness detection in fingerprint systems a new database with fine details of fingertips is needed. Therefore a new high resolution Brunel Fingerprint Biometric Database (B-FBDB) was captured and collected for this novel liveness detection research. The first proposed novel liveness detection method is a High Pass Correlation Filtering Algorithm (HCFA). This image processing algorithm has been developed in Matlab and tested on B-FBDB dataset images. The results of the HCFA algorithm have proved the idea behind the research, as they successfully demonstrated the clear possibility of liveness detection by active pore detection from high resolution images. The second novel liveness detection method is based on the experimental evidence. This method explains liveness detection by measuring the ionic activities above the sample of ionic sweat fluid. A Micro Needle Electrode (MNE) based setup was used in this experiment to measure the ionic activities. In results, 5.9 pC to 6.5 pC charges were detected with ten NME positions (50μm to 360 μm) above the surface of ionic sweat fluid. These measurements are also a proof of liveness from active fingertip pores, and this technique can be used in the future to implement liveness detection solutions. The interaction of NME and ionic fluid was modelled in COMSOL multiphysics, and the effect of electric field variations on NME was recorded at 5μm -360μm positions above the ionic fluid.
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Dohnálek, Tomáš. "Liveness Detection on Fingers Using Vein Pattern." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234901.

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Tato práce se zabývá rozšířením snímače otisků prstů Touchless Biometric Systems 3D-Enroll o jednotku detekce živosti prstu na základě žil. Bylo navrhnuto a zkonstruováno hardwarové řešení s využitím infračervených diod. Navržené softwarové řešení pracuje ve dvou různých režimech: detekce živosti na základě texturních příznaků a verifikace uživatelů na základě porovnávání žilních vzorů. Datový soubor obsahující přes 1100 snímků jak živých prstů tak jejich falsifikátů vznikl jako součást této práce a výkonnost obou zmíněných režimů byla vyhodnocena na tomto datovém souboru. Na závěr byly navrhnuty materiály vhodné k výrobě falsifikátů otisků prstů umožňující oklamání detekce živosti pomocí žilních vzorů.
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Nogueira, Rodrigo Frassetto 1986. "Software based fingerprint liveness detection = Detecção de vivacidade de impressões digitais baseada em software." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259824.

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Orientador: Roberto de Alencar Lotufo
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-26T03:01:45Z (GMT). No. of bitstreams: 1 Nogueira_RodrigoFrassetto_M.pdf: 3122263 bytes, checksum: e6333eb55b8b4830e318721882159cd1 (MD5) Previous issue date: 2014
Resumo: Com o uso crescente de sistemas de autenticação por biometria nos últimos anos, a detecção de impressões digitais falsas tem se tornado cada vez mais importante. Neste trabalho, nós implementamos e comparamos várias técnicas baseadas em software para detecção de vivacidade de impressões digitais. Utilizamos como extratores de características as redes convolucionais, que foram usadas pela primeira vez nesta área, e Local Binary Patterns (LBP). As técnicas foram usadas em conjunto com redução de dimensionalidade através da Análise de Componentes Principais (PCA) e um classificador Support Vector Machine (SVM). O aumento artificial de dados foi usado de forma bem sucedida para melhorar o desempenho do classificador. Testamos uma variedade de operações de pré-processamento, tais como filtragem em frequência, equalização de contraste e filtragem da região de interesse. Graças aos computadores de alto desempenho disponíveis como serviços em nuvem, foi possível realizar uma busca extensa e automática para encontrar a melhor combinação de operações de pré-processamento, arquiteturas e hiper-parâmetros. Os experimentos foram realizados nos conjuntos de dados usados nas competições Liveness Detection nos anos de 2009, 2011 e 2013, que juntos somam quase 50.000 imagens de impressões digitais falsas e verdadeiras. Nosso melhor método atinge uma taxa média de amostras classificadas corretamente de 95,2%, o que representa uma melhora de 59% na taxa de erro quando comparado com os melhores resultados publicados anteriormente
Abstract: With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implemented and compared various techniques for software-based fingerprint liveness detection. We use as feature extractors Convolutional Networks with random weights, which are applied for the first time for this task, and Local Binary Patterns. The techniques were used in conjunction with dimensionality reduction through Principal Component Analysis (PCA) and a Support Vector Machine (SVM) classifier. Dataset Augmentation was successfully used to increase classifier¿s performance. We tested a variety of preprocessing operations such as frequency filtering, contrast equalization, and region of interest filtering. An automatic and extensive search for the best combination of preprocessing operations, architectures and hyper-parameters was made, thanks to the fast computers available as cloud services. The experiments were made on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013 that comprise almost 50,000 real and fake fingerprints¿ images. Our best method achieves an overall rate of 95.2% of correctly classified samples - an improvement of 59% in test error when compared with the best previously published results
Mestrado
Energia Eletrica
Mestre em Engenharia Elétrica
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Brabec, Lukáš. "Biometrická detekce živosti pro technologii rozpoznávání otisků prstů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234955.

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This work focuses on liveness detection for the fingerprint recognition technology. The first part of this thesis describes biometrics, biometric systems, liveness detection and the method for liveness detection is proposed, which is based on spectroscopic characteristics of human skin. The second part describes and summarizes performed experiments. In the end, the results are discussed and further improvements are proposed.
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Jurek, Jakub. "Biometrické rozpoznání živosti prstu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242191.

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This project deals with general biometrics issues focusing on fingerprint biometrics, with description of dermal papillae and principles of fingerprint sensors. Next this work deals with fingerprint liveness detection issues, including description of methods of detection. Next this work describes chosen features for own detection, used database of fingerprints and own algorithm for image pre-processing. Furthermore neural network classifier for liveness detection with chosen features is decribed followed by statistic evaluation of the chosen features and detection results as well as description of the created graphical user interface.
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Váňa, Tomáš. "Biometrické rozpoznání živosti prstu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221380.

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This master‘s thesis deals with biometric fingerprint liveness detection. The theoretical part of the work describes fingerprint recognition biometric systems, fingerprint liveness detection issues and methods for fingerprint liveness detection. The practical part of the work describes proposed set of discriminant features and preprocessing of fingerprint image. Proposed approach using neural network to detect a liveness. The algorithm is tested on LivDet database comprising real and fake images acquired with tree sensors. Classification performance approximately 93% was obtained.
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Lichvár, Michal. "Detekce živosti prstu na základě změn papilárních linií." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-236005.

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There are several frauds against biometric systems (BSs) and several techniques exist to secure BSs against these frauds. One of the techniques is liveness detection. To fool fingerprint sensors, latent fingerprints, dummy fingers and wafer-thin layer attached to the finger are being used. Liveness detection is being used also when scanning fingerprints. Several different characteristics of the live finger can be used to detect liveness, for example sweat, conductivity etc. In this thesis, new approach is examined. It is based on the expandability of the finger as an effect of heartbeats/pulsation. As the skin is expanding, also the distances between papillary lines are expanding. Whole finger expands approximately in range of 4,5 ľm , the distance between two neighbor papillary lines in 0,454 ľm . This value collides with wavelength of blue and green light. The result from this work is following. The resolution of the capturing device is not high enough to capture the expandability on distance between two neighbor papillary lines. Also, because of collision with wavelength, the diffraction effect is presented and the result images are influenced by this error.
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Lodrová, Dana. "Bezpečnost biometrických systémů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-261226.

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Hlavním přínosem této práce jsou dva nové přístupy pro zvýšení bezpečnosti biometrických systémů založených na rozpoznávání podle otisků prstů. První přístup je z oblasti testování živosti a znemožňuje použití různých typů falešných otisků prstů a jiných metod oklamání senzoru v průběhu procesu snímání otisků. Tento patentovaný přístup je založen na změně barvy a šířky papilárních linií vlivem přitlačení prstu na skleněný podklad. Výsledná jednotka pro testování živosti může být integrována do optických senzorů.  Druhý přístup je z oblasti standardizace a zvyšuje bezpečnost a interoperabilitu procesů extrakce markantů a porovnání. Pro tyto účely jsem vytvořila metodologii, která stanovuje míry sémantické shody pro extraktory markantů otisků prstů. Markanty nalezené testovanými extraktory jsou porovnávány oproti Ground-Truth markantům získaným pomocí shlukování dat poskytnutých daktyloskopickými experty. Tato navrhovaná metodologie je zahrnuta v navrhovaném dodatku k normě ISO/IEC 29109-2 (Amd. 2 WD4).
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Book chapters on the topic "Fingerprint Liveness Detection"

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Schuckers, Stephanie A. C. "Liveness Detection: Fingerprint." In Encyclopedia of Biometrics, 924–31. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_68.

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Johnson, Peter, and Stephanie Schuckers. "Fingerprint Spoofing and Liveness Detection." In Forensic Science, 373–82. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527693535.ch16.

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Ghiani, Luca, Paolo Denti, and Gian Luca Marcialis. "Experimental Results on Fingerprint Liveness Detection." In Articulated Motion and Deformable Objects, 210–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31567-1_21.

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Toosi, Amirhosein, Sandro Cumani, and Andrea Bottino. "On Multiview Analysis for Fingerprint Liveness Detection." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 143–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25751-8_18.

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Sharma, Ram Prakash, Ashutosh Anshul, Ashwini Jha, and Somnath Dey. "Investigating Fingerprint Quality Features for Liveness Detection." In Mining Intelligence and Knowledge Exploration, 296–307. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66187-8_28.

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Lu, Mengya, Zhiqiang Chen, and Weiguo Sheng. "Fingerprint Liveness Detection Based on Pore Analysis." In Biometric Recognition, 233–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25417-3_28.

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Marcialis, Gian Luca, Luca Ghiani, Katja Vetter, Dirk Morgeneier, and Fabio Roli. "Large Scale Experiments on Fingerprint Liveness Detection." In Lecture Notes in Computer Science, 501–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_55.

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Wang, Feng, Jian Cheng, and Yan Jiang. "Ridge-Slope-Valley Feature for Fingerprint Liveness Detection." In Lecture Notes in Electrical Engineering, 857–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_90.

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Kumar, Munish, and Priyanka Singh. "Liveness Detection and Recognition System for Fingerprint Images." In Lecture Notes in Networks and Systems, 467–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3172-9_45.

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Marcialis, Gian Luca, Aaron Lewicke, Bozhao Tan, Pietro Coli, Dominic Grimberg, Alberto Congiu, Alessandra Tidu, Fabio Roli, and Stephanie Schuckers. "First International Fingerprint Liveness Detection Competition—LivDet 2009." In Image Analysis and Processing – ICIAP 2009, 12–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_4.

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Conference papers on the topic "Fingerprint Liveness Detection"

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Akhtar, Zahid, Christian Micheloni, and Gian Luca Foresti. "Correlation based fingerprint liveness detection." In 2015 International Conference on Biometrics (ICB). IEEE, 2015. http://dx.doi.org/10.1109/icb.2015.7139054.

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Al-Ajlan, Amani. "Survey on fingerprint liveness detection." In 2013 International Workshop on Biometrics and Forensics (IWBF 2013). IEEE, 2013. http://dx.doi.org/10.1109/iwbf.2013.6547309.

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Ozkiper, Zeynep Inel, Zeynep Turgut, Tulin Atmaca, and Muhammed Ali Aydin. "Fingerprint Liveness Detection Using Deep Learning." In 2022 9th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2022. http://dx.doi.org/10.1109/ficloud57274.2022.00025.

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Mura, Valerio, Luca Ghiani, Gian Luca Marcialis, Fabio Roli, David A. Yambay, and Stephanie A. Schuckers. "LivDet 2015 fingerprint liveness detection competition 2015." In 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2015. http://dx.doi.org/10.1109/btas.2015.7358776.

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Yambay, David, Stephanie Schuckers, Samantha Denning, Constantin Sandmann, Andrey Bachurinski, and Josh Hogan. "LivDet 2017 - Fingerprint Systems Liveness Detection Competition." In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2018. http://dx.doi.org/10.1109/btas.2018.8698578.

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Galbally, Javier, Fernando Alonso-Fernandez, Julian Fierrez, and Javier Ortega-Garcia. "Fingerprint liveness detection based on quality measures." In 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS). IEEE, 2009. http://dx.doi.org/10.1109/bids.2009.5507534.

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Ghiani, Luca, David Yambay, Valerio Mura, Simona Tocco, Gian Luca Marcialis, Fabio Roli, and Stephanie Schuckcrs. "LivDet 2013 Fingerprint Liveness Detection Competition 2013." In 2013 International Conference on Biometrics (ICB). IEEE, 2013. http://dx.doi.org/10.1109/icb.2013.6613027.

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Mura, Valerio, Giulia Orru, Roberto Casula, Alessandra Sibiriu, Giulia Loi, Pierluigi Tuveri, Luca Ghiani, and Gian Luca Marcialis. "LivDet 2017 Fingerprint Liveness Detection Competition 2017." In 2018 International Conference on Biometrics (ICB). IEEE, 2018. http://dx.doi.org/10.1109/icb2018.2018.00052.

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Arunalatha, G., and M. Ezhilarasan. "Fingerprint Liveness detection using probabality density function." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754121.

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Lazimul, Limnd T. P., and D. L. Binoy. "Fingerprint liveness detection using convolutional neural network and fingerprint image enhancement." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389533.

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