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Relevant bibliographies by topics / Multibiometric systems

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Academic literature on the topic 'Multibiometric systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "Multibiometric systems"

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Jain, Anil K., and Arun Ross. "Multibiometric systems." Communications of the ACM 47, no. 1 (January 1, 2004): 34. http://dx.doi.org/10.1145/962081.962102.

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Nair, Suresh Kumar Ramachandran, Bir Bhanu, Subir Ghosh, and Ninad S. Thakoor. "Predictive models for multibiometric systems." Pattern Recognition 47, no. 12 (December 2014): 3779–92. http://dx.doi.org/10.1016/j.patcog.2014.05.020.

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AlMahafzah, Harbi, and Maen Zaid AlRwashdeh. "A Survey of Multibiometric Systems." International Journal of Computer Applications 43, no. 15 (April 30, 2012): 36–43. http://dx.doi.org/10.5120/6182-8612.

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Li, Yong, Jian Ping Yin, and En Zhu. "An Evaluation Survey of Score Normalization in Multibiometric Systems." Advanced Engineering Forum 1 (September 2011): 168–72. http://dx.doi.org/10.4028/www.scientific.net/aef.1.168.

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Multibiometric fusion is an active research area for many years. Score normalization is to transform the scores from different matchers to a common domain. In this paper, we give a survey of classical score normalization techniques and recent advances of this research area. The performance of different normalization functions, such as MinMax, Tanh, Zscore, PL, LTL, RHE and FF are evaluated in XM2VTS Benchmark. We evaluated the performance with four different measures of biometric systems such as EER, AUC, GAR(FAR=0.001) and the threshold of EER. The experimental results show that there is no single normalization technique that would perform the best for all multibiometric recognition systems. PL and FF normalization outperform other methods in many applications.

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NisarBhat, Asra, and Supreet Kaur. "Enhancement of Biometric Template Security in Multibiometric Systems." International Journal of Computer Applications 69, no. 10 (May 17, 2013): 36–41. http://dx.doi.org/10.5120/11882-7698.

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Canuto, Anne Magaly de Paula, Michael C. Fairhurst, and Fernando Pintro. "Ensemble systems and cancellable transformations for multibiometric‐based identification." IET Biometrics 3, no. 1 (March 2014): 29–40. http://dx.doi.org/10.1049/iet-bmt.2012.0032.

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Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, Noubeil Guermat, Lahcene Ziet, Mohamed Cheniti, and Khan Muhammad. "Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition." Symmetry 12, no. 3 (March 10, 2020): 444. http://dx.doi.org/10.3390/sym12030444.

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast.

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Hariri, Mahdi. "Possibility of spoof attack against robustness of multibiometric authentication systems." Optical Engineering 50, no. 7 (July 1, 2011): 079001. http://dx.doi.org/10.1117/1.3599874.

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Biggio, Battista, Giorgio Fumera, Gian Luca Marcialis, and Fabio Roli. "Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 3 (March 1, 2017): 561–75. http://dx.doi.org/10.1109/tpami.2016.2558154.

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Roy, Kaushik, Brian O'Connor, Foysal Ahmad, and Mohamed S. Kamel. "Multibiometric System Using Level Set, Modified LBP and Random Forest." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450013. http://dx.doi.org/10.1142/s0219467814500132.

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Multibiometric systems alleviate some of the shortcomings possessed by the unimodal biometrics and provide better recognition performance. This paper presents a multibiometric system that integrates the iris and face features based on the fusion at the feature level. The proposed multibiometric system has three novelties as compared to the previous works. First, distance regularized level-set evolution (DRLSE) technique is utilized to localize the iris and pupil boundary from an iris image. The DRLSE maintains the regularity of the level set function intrinsically during the curve evolution process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Second, a modified local binary pattern (MLBP), which combines both the sign and magnitude features for the improvement of recognition performance, is applied. Third, to select the optimal subset of features from the fused feature vector, a feature subset selection scheme based on random forest (RF) is proposed. To evaluate the performance of the proposed scheme, the facial images of Yale Extended B Face database are fused with the iris images of CASIA V4 interval dataset to construct an iris–face multimodal biometric dataset. The experimental results indicate that the proposed multimodal biometrics system is more reliable and robust than the unimodal biometric scheme.

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Dissertations / Theses on the topic "Multibiometric systems"

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Dhamala, Pushpa. "Multibiometric Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for telematikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18895.

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Sepasian, Mojtaba. "Multibiometric security in wireless communication systems." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/5081.

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This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims.

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Nandakumar, Karthik. "Multibiometric systems fusion strategies and template security /." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science and Engineering, 2008.
Title from PDF t.p. (viewed on Mar. 30, 2009) Includes bibliographical references (p. 210-228). Also issued in print.

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Janečka, Petr. "Multimodální biometrický systém kombinující duhovku a sítnici." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234910.

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This diploma thesis focuses on multibiometric systems, specifically on biometric fusion. The thesis describes eye biometrics, i.e. recognition based on retina and iris. The key part consists of design and implementation specification of a biometric system based on retina and iris recognition.

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Nassar, Alaa S. N. "A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16917.

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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.
Higher Committee for Education Development in Iraq

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Junior, Jozias Rolim de Araújo. "Reconhecimento multibiométrico baseado em imagens de face parcialmente ocluídas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-24122018-011508/.

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Com o avanço da tecnologia, as estratégias tradicionais para identificação de pessoas se tornaram mais suscetíveis a falhas. De forma a superar essas dificuldades algumas abordagens vêm sendo propostas na literatura. Dentre estas abordagens destaca-se a Biometria. O campo da Biometria abarca uma grande variedade de tecnologias usadas para identificar ou verificar a identidade de uma pessoa por meio da mensuração e análise de aspectos físicos e/ou comportamentais do ser humano. Em função disso, a biometria tem um amplo campo de aplicações em sistemas que exigem uma identificação segura de seus usuários. Os sistemas biométricos mais populares são baseados em reconhecimento facial ou em impressões digitais. Entretanto, existem sistemas biométricos que utilizam a íris, varredura de retina, voz, geometria da mão e termogramas faciais. Atualmente, tem havido progresso significativo em reconhecimento automático de face em condições controladas. Em aplicações do mundo real, o reconhecimento facial sofre de uma série de problemas nos cenários não controlados. Esses problemas são devidos, principalmente, a diferentes variações faciais que podem mudar muito a aparência da face, incluindo variações de expressão, de iluminação, alterações da pose, assim como oclusões parciais. Em comparação com o grande número de trabalhos na literatura em relação aos problemas de variação de expressão/iluminação/pose, o problema de oclusão é relativamente negligenciado pela comunidade científica. Embora tenha sido dada pouca atenção ao problema de oclusão na literatura de reconhecimento facial, a importância deste problema deve ser enfatizada, pois a presença de oclusão é muito comum em cenários não controlados e pode estar associada a várias questões de segurança. Por outro lado, a Multibiométria é uma abordagem relativamente nova para representação de conhecimento biométrico que visa consolida múltiplas fontes de informação visando melhorar a performance do sistema biométrico. Multibiométria é baseada no conceito de que informações obtidas a partir de diferentes modalidades ou da mesma modalidade capturada de diversas formas se complementam. Consequentemente, uma combinação adequada dessas informações pode ser mais útil que o uso de informações obtidas a partir de qualquer uma das modalidades individualmente. A fim de melhorar a performance dos sistemas biométricos faciais na presença de oclusão parciais será investigado o emprego de diferentes técnicas de reconstrução de oclusões parciais de forma a gerar diferentes imagens de face, as quais serão combinadas no nível de extração de característica e utilizadas como entrada para um classificador neural. Os resultados demonstram que a abordagem proposta é capaz de melhorar a performance dos sistemas biométricos baseados em face parcialmente ocluídas
With the advancement of technology, traditional strategies for identifying people have become more susceptible to failures. In order to overcome these difficulties, some approaches have been proposed in the literature. Among these approaches, Biometrics stands out. The field of biometrics covers a wide range of technologies used to identify or verify a person\'s identity by measuring and analyzing physical and / or behavioral aspects of the human being. As a result, a biometry has a wide field of applications in systems that require a secure identification of its users. The most popular biometric systems are based on facial recognition or fingerprints. However, there are biometric systems that use the iris, retinal scan, voice, hand geometry, and facial thermograms. Currently, there has been significant progress in automatic face recognition under controlled conditions. In real world applications, facial recognition suffers from a number of problems in uncontrolled scenarios. These problems are mainly due to different facial variations that can greatly change the appearance of the face, including variations in expression, illumination, posture, as well as partial occlusions. Compared with the large number of papers in the literature regarding problems of expression / illumination / pose variation, the occlusion problem is relatively neglected by the research community. Although attention has been paid to the occlusion problem in the facial recognition literature, the importance of this problem should be emphasized, since the presence of occlusion is very common in uncontrolled scenarios and may be associated with several safety issues. On the other hand, multibiometry is a relatively new approach to biometric knowledge representation that aims to consolidate multiple sources of information to improve the performance of the biometric system. Multibiometry is based on the concept that information obtained from different modalities or from the same modalities captured in different ways complement each other. Accordingly, a suitable combination of such information may be more useful than the use of information obtained from any of the individuals modalities. In order to improve the performance of facial biometric systems in the presence of partial occlusion, the use of different partial occlusion reconstruction techniques was investigated in order to generate different face images, which were combined at the feature extraction level and used as input for a neural classifier. The results demonstrate that the proposed approach is capable of improving the performance of biometric systems based on partially occluded faces

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Book chapters on the topic "Multibiometric systems"

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Shyam, Radhey, and Yogendra Narain Singh. "Robustness of Score Normalization in Multibiometric Systems." In Information Systems Security, 542–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26961-0_33.

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Ross, Arun, and Norman Poh. "Multibiometric Systems: Overview, Case Studies, and Open Issues." In Advances in Pattern Recognition, 273–92. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-385-3_11.

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Bahmed, Farah, and Madani Ould Mammar. "A Survey on Hand Modalities and Hand Multibiometric Systems." In Innovations in Smart Cities Applications Edition 3, 73–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37629-1_7.

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Nandakumar, Karthik, Anil K. Jain, and Arun Ross. "Fusion in Multibiometric Identification Systems: What about the Missing Data?" In Advances in Biometrics, 743–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_76.

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Gudavalli, Madhavi, D. Srinivasa Kumar, and S. Viswanadha Raju. "A Multibiometric Fingerprint Recognition System Based on the Fusion of Minutiae and Ridges." In Advances in Intelligent Systems and Computing, 231–37. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13728-5_26.

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Marasco, Emanuela, Peter Johnson, Carlo Sansone, and Stephanie Schuckers. "Increase the Security of Multibiometric Systems by Incorporating a Spoofing Detection Algorithm in the Fusion Mechanism." In Multiple Classifier Systems, 309–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21557-5_33.

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Garcia-Salicetti, Sonia, Mohamed Anouar Mellakh, Lorène Allano, and Bernadette Dorizzi. "A Generic Protocol for Multibiometric Systems Evaluation on Virtual and Real Subjects." In Lecture Notes in Computer Science, 494–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527923_51.

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Lip, Chia Chin, and Dzati Athiar Ramli. "Comparative Study on Feature, Score and Decision Level Fusion Schemes for Robust Multibiometric Systems." In Frontiers in Computer Education, 941–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27552-4_123.

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Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Issues in Biometric System and Proposed Research Methodology." In Multibiometric Watermarking with Compressive Sensing Theory, 47–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_3.

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Ramli, Dzati Athiar, Salina Abdul Samad, and Aini Hussain. "An Adaptive Multibiometric System for Uncertain Audio Condition." In Lecture Notes in Electrical Engineering, 165–77. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8776-8_15.

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Conference papers on the topic "Multibiometric systems"

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Ghouti, Lahouari, and Ahmed A. Bahjat. "Iris fusion for multibiometric systems." In 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2009. http://dx.doi.org/10.1109/isspit.2009.5407577.

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Sharma, Renu, Sukhendu Das, and Padmaja Joshi. "Rank level fusion in multibiometric systems." In 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2015. http://dx.doi.org/10.1109/ncvpripg.2015.7489952.

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Nandakumar, Karthik, and Anil K. Jain. "Multibiometric Template Security Using Fuzzy Vault." In 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems. IEEE, 2008. http://dx.doi.org/10.1109/btas.2008.4699352.

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Abaza, Ayman, and Arun Ross. "Quality based rank-level fusion in multibiometric systems." In 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (BTAS). IEEE, 2009. http://dx.doi.org/10.1109/btas.2009.5339081.

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Nandakumar, K., Yi Chen, A. K. Jain, and S. C. Dass. "Quality-based Score Level Fusion in Multibiometric Systems." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.951.

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De Maio, Luigi, Riccardo Distasi, and Michele Nappi. "MUBIDUS-I: A multibiometric and multipurpose dataset." In 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2019. http://dx.doi.org/10.1109/sitis.2019.00124.

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Abbas, Nassim, Messaoud Bengherabi, and Elhocine Boutellaa. "Experimental investigation of OC-SVM for multibiometric score fusion." In 2013 8th InternationalWorkshop on Systems, Signal Processing and their Applications (WoSSPA). IEEE, 2013. http://dx.doi.org/10.1109/wosspa.2013.6602371.

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Imran, Mohammad, Ashok Rao, and G. Hemantha Kumar. "A New Hybrid Approach for Information Fusion in Multibiometric Systems." In 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2011. http://dx.doi.org/10.1109/ncvpripg.2011.57.

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Akhtar, Zahid, Giorgio Fumera, Gian Luca Marcialis, and Fabio Roli. "Evaluation of serial and parallel multibiometric systems under spoofing attacks." In 2012 IEEE Fifth International Conference On Biometrics: Theory, Applications And Systems (BTAS). IEEE, 2012. http://dx.doi.org/10.1109/btas.2012.6374590.

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Dehache, Ismahene, and Labiba Souici-Meslati. "A multibiometric system for identity verification based on fingerprints and signatures." In 2012 International Conference on Complex Systems (ICCS). IEEE, 2012. http://dx.doi.org/10.1109/icocs.2012.6458529.

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