Literatura académica sobre el tema "Fingerprint Liveness Detection"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Fingerprint Liveness Detection".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Fingerprint Liveness Detection"
Jiang, Yujia y 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.
Texto completoNIKAM, SHANKAR BHAUSAHEB y SUNEETA AGARWAL. "CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION". International Journal of Image and Graphics 09, n.º 02 (abril de 2009): 171–99. http://dx.doi.org/10.1142/s0219467809003393.
Texto completoLee, Youn Kyu, Jongwook Jeong y Dongwoo Kang. "An Effective Orchestration for Fingerprint Presentation Attack Detection". Electronics 11, n.º 16 (11 de agosto de 2022): 2515. http://dx.doi.org/10.3390/electronics11162515.
Texto completoBabikir Adam, Edriss Eisa y Sathesh. "Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View". Journal of ISMAC 3, n.º 1 (1 de marzo de 2021): 16–30. http://dx.doi.org/10.36548/jismac.2021.1.002.
Texto completoAlmehmadi, Abdulaziz. "A Behavioral-Based Fingerprint Liveness and Willingness Detection System". Applied Sciences 12, n.º 22 (11 de noviembre de 2022): 11460. http://dx.doi.org/10.3390/app122211460.
Texto completoGuo, Yanyan, Xiangdong Fei y Qijun Zhao. "Fingerprint Liveness Detection Using Multiple Static Features and Random Forests". International Journal of Image and Graphics 14, n.º 04 (octubre de 2014): 1450021. http://dx.doi.org/10.1142/s0219467814500211.
Texto completoF.W. Onifade, Olufade, Paul Akinde y Folasade Olubusola Isinkaye. "Circular Gabor wavelet algorithm for fingerprint liveness detection". Journal of Advanced Computer Science & Technology 9, n.º 1 (11 de enero de 2020): 1. http://dx.doi.org/10.14419/jacst.v9i1.29908.
Texto completoMoon, Y. S., J. S. Chen, K. C. Chan, K. So y K. C. Woo. "Wavelet based fingerprint liveness detection". Electronics Letters 41, n.º 20 (2005): 1112. http://dx.doi.org/10.1049/el:20052577.
Texto completoRani, Rajneesh y Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach". International Journal of Intelligent Information Technologies 17, n.º 1 (enero de 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.
Texto completoDrahansky, Martin, Michal Dolezel, Jan Vana, Eva Brezinova, Jaegeol Yim y 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.
Texto completoTesis sobre el tema "Fingerprint Liveness Detection"
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.
Texto completoBiometrics 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.
GHIANI, LUCA. "Textural features for fingerprint liveness detection". Doctoral thesis, Università degli Studi di Cagliari, 2015. http://hdl.handle.net/11584/266594.
Texto completoMemon, 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.
Texto completoDohnálek, 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.
Texto completoNogueira, 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.
Texto completoDissertação (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
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.
Texto completoJurek, 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.
Texto completoVáňa, 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.
Texto completoLichvár, 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.
Texto completoLodrová, Dana. "Bezpečnost biometrických systémů". Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-261226.
Texto completoCapítulos de libros sobre el tema "Fingerprint Liveness Detection"
Schuckers, Stephanie A. C. "Liveness Detection: Fingerprint". En Encyclopedia of Biometrics, 924–31. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_68.
Texto completoJohnson, Peter y Stephanie Schuckers. "Fingerprint Spoofing and Liveness Detection". En Forensic Science, 373–82. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527693535.ch16.
Texto completoGhiani, Luca, Paolo Denti y Gian Luca Marcialis. "Experimental Results on Fingerprint Liveness Detection". En 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.
Texto completoToosi, Amirhosein, Sandro Cumani y Andrea Bottino. "On Multiview Analysis for Fingerprint Liveness Detection". En 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.
Texto completoSharma, Ram Prakash, Ashutosh Anshul, Ashwini Jha y Somnath Dey. "Investigating Fingerprint Quality Features for Liveness Detection". En Mining Intelligence and Knowledge Exploration, 296–307. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66187-8_28.
Texto completoLu, Mengya, Zhiqiang Chen y Weiguo Sheng. "Fingerprint Liveness Detection Based on Pore Analysis". En Biometric Recognition, 233–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25417-3_28.
Texto completoMarcialis, Gian Luca, Luca Ghiani, Katja Vetter, Dirk Morgeneier y Fabio Roli. "Large Scale Experiments on Fingerprint Liveness Detection". En 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.
Texto completoWang, Feng, Jian Cheng y Yan Jiang. "Ridge-Slope-Valley Feature for Fingerprint Liveness Detection". En Lecture Notes in Electrical Engineering, 857–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_90.
Texto completoKumar, Munish y Priyanka Singh. "Liveness Detection and Recognition System for Fingerprint Images". En Lecture Notes in Networks and Systems, 467–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3172-9_45.
Texto completoMarcialis, Gian Luca, Aaron Lewicke, Bozhao Tan, Pietro Coli, Dominic Grimberg, Alberto Congiu, Alessandra Tidu, Fabio Roli y Stephanie Schuckers. "First International Fingerprint Liveness Detection Competition—LivDet 2009". En 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.
Texto completoActas de conferencias sobre el tema "Fingerprint Liveness Detection"
Akhtar, Zahid, Christian Micheloni y Gian Luca Foresti. "Correlation based fingerprint liveness detection". En 2015 International Conference on Biometrics (ICB). IEEE, 2015. http://dx.doi.org/10.1109/icb.2015.7139054.
Texto completoAl-Ajlan, Amani. "Survey on fingerprint liveness detection". En 2013 International Workshop on Biometrics and Forensics (IWBF 2013). IEEE, 2013. http://dx.doi.org/10.1109/iwbf.2013.6547309.
Texto completoOzkiper, Zeynep Inel, Zeynep Turgut, Tulin Atmaca y Muhammed Ali Aydin. "Fingerprint Liveness Detection Using Deep Learning". En 2022 9th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2022. http://dx.doi.org/10.1109/ficloud57274.2022.00025.
Texto completoMura, Valerio, Luca Ghiani, Gian Luca Marcialis, Fabio Roli, David A. Yambay y Stephanie A. Schuckers. "LivDet 2015 fingerprint liveness detection competition 2015". En 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2015. http://dx.doi.org/10.1109/btas.2015.7358776.
Texto completoYambay, David, Stephanie Schuckers, Samantha Denning, Constantin Sandmann, Andrey Bachurinski y Josh Hogan. "LivDet 2017 - Fingerprint Systems Liveness Detection Competition". En 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2018. http://dx.doi.org/10.1109/btas.2018.8698578.
Texto completoGalbally, Javier, Fernando Alonso-Fernandez, Julian Fierrez y Javier Ortega-Garcia. "Fingerprint liveness detection based on quality measures". En 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS). IEEE, 2009. http://dx.doi.org/10.1109/bids.2009.5507534.
Texto completoGhiani, Luca, David Yambay, Valerio Mura, Simona Tocco, Gian Luca Marcialis, Fabio Roli y Stephanie Schuckcrs. "LivDet 2013 Fingerprint Liveness Detection Competition 2013". En 2013 International Conference on Biometrics (ICB). IEEE, 2013. http://dx.doi.org/10.1109/icb.2013.6613027.
Texto completoMura, Valerio, Giulia Orru, Roberto Casula, Alessandra Sibiriu, Giulia Loi, Pierluigi Tuveri, Luca Ghiani y Gian Luca Marcialis. "LivDet 2017 Fingerprint Liveness Detection Competition 2017". En 2018 International Conference on Biometrics (ICB). IEEE, 2018. http://dx.doi.org/10.1109/icb2018.2018.00052.
Texto completoArunalatha, G. y M. Ezhilarasan. "Fingerprint Liveness detection using probabality density function". En 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754121.
Texto completoLazimul, Limnd T. P. y D. L. Binoy. "Fingerprint liveness detection using convolutional neural network and fingerprint image enhancement". En 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389533.
Texto completo