Relevant bibliographies by topics / Biometric identification systems

Academic literature on the topic 'Biometric identification systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Biometric identification systems"

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Shopon, Md, Sanjida Nasreen Tumpa, Yajurv Bhatia, K. N. Pavan Kumar, and Marina L. Gavrilova. "Biometric Systems De-Identification: Current Advancements and Future Directions." Journal of Cybersecurity and Privacy 1, no. 3 (August 31, 2021): 470–95. http://dx.doi.org/10.3390/jcp1030024.

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Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.
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Kulikov, A. A. "Application of biometric systems in face identification technologies." Russian Technological Journal 9, no. 3 (June 28, 2021): 7–14. http://dx.doi.org/10.32362/2500-316x-2021-9-3-7-14.

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The paper presents an analytical review of the application of biometric recognition systems in relation to facial image identification technologies. The classification of biometric systems is presented. The trends of technological progress in the field of biometrics and facial recognition capabilities are considered. It is determined that in 2020 there is a trend of transition from the use of biometric recognition technologies in traditional state security systems to the sphere of commercial and user applications. The process of «linking» encryption keys and passwords with the biometric parameters of the data subject is described. It is proposed that a biometric feature and a biometrics parameter mean a certain value that has a physical meaning that characterizes the subject itself. The possibility of using circular neighborhood and bilinear interpolation of pixel intensity values in biometrics is also presented. This will make it possible to build a local binary template. In order to solve the problem of identification of persons, it is advisable to investigate the essence of biometric systems in the technologies of identification of persons, their types, identifying the shortcomings of each of them, on the basis of which to present the directions of elimination and search for the most reliable technologies. The essence of the use of biometric systems in the technologies of identification of persons is, for example, that the user can provide the bank or other counterparty with evidence that it is he who wants to use the services on his accounts. At the same time, the demand has increased for contactless biometric solutions. These technologies are implemented in order to conduct additional biometric verification of users. This allows to minimize possible fraud or violation of the internal rules of the service, for example, the transfer of accounts of some registered users to others.
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Reddy, M. V. Bramhananda, and V. Goutham. "IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION." International Journal of Research -GRANTHAALAYAH 6, no. 1 (January 31, 2018): 80–90. http://dx.doi.org/10.29121/granthaalayah.v6.i1.2018.1596.

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Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms. Since iris is unique for all individuals across the globe, many researchers focused on using iris or along with other biometrics for security with great precision. Multimodal biometric systems came into existence for better accuracy in human authentication. However, iris is considered to be most discriminatory of facial biometrics. Study of iris based human identification in ideal and non-cooperative environments can provide great insights which can help researchers and organizations that depend on iris-based biometric systems. The technical knowhow of iris strengths and weaknesses can be great advantage. This is more important in the wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.
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., Himanshi, Trisha Gulati, and Yasha Hasija. "Biometrics in Healthcare." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 3, no. 2 (April 15, 2018): 13–17. http://dx.doi.org/10.35121/ijapie201804223.

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Biometrics is the discipline to measure physical human characteristics for the identification and authentication of an individual. Since ancient times, people have used voice, face, and other characteristics for the identification of an individual. With evolution, we take the individual characteristics like fingerprint scans, retina and iris images, etc., as inputs to the computer systems and then store or verify them with existing records. This report discusses biometrics and its recent roles found in the field of healthcare, medicine, genetics, and biotechnology. It includes the concept of biometrics, the system used for biometric recognition and its working, types of biometric systems, the different system algorithms applied, and system modules which are well illustrated with flow charts and block diagrams. Some of the health institutes in developed countries have started using biometric systems for checking patients and/or doctors. Biometry has enabled the proper organization and storage of the health records of individuals in medical institutes. Biometric authentication is also finding a distinct role in foiling medical claims fraud highlighting the advantages it. Even after processing via a very accurate biometric system, there is a chance of a false result due to some disease or injury to the body part subjected to biometry or faulty system leading to some error. There is also a possibility that the biometric system may harm our bodies. Moreover, biometric records need really tight system security to prevent any kind of misuse. Biometrics has a great potential to find a lot more uses in the field of healthcare. Many ideas are being proposed for implementation. In the future, biometrics can be used to detect potential disease and risks by using methods like adiposity measurement and Gas Discharge Visualization (GDV).
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de Luis-Garcı́a, Rodrigo, Carlos Alberola-López, Otman Aghzout, and Juan Ruiz-Alzola. "Biometric identification systems." Signal Processing 83, no. 12 (December 2003): 2539–57. http://dx.doi.org/10.1016/j.sigpro.2003.08.001.

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Rajvanshi, Saumya, Shiv Chauhan, and Savneet Kaur. "A New Wave in Biometric System: Systematic Study." CGC International Journal of Contemporary Technology and Research 4, no. 2 (August 5, 2022): 300–305. http://dx.doi.org/10.46860/cgcijctr.2022.07.31.300.

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Biometric system is a technique used to identify a person using its personal identification methods. The main concept of biometric systems is to provide confidentiality and security to the user. A number of biometric systems are introduced but some systems are widely used and are famous because of their usage and security they provide. Physiological and Behavioral biometrics are the two types of biometric systems. Biometric systems include physiological biometrics like face recognition, fingerprint recognition, iris recognition and behavioral biometrics like signature recognition and voice recognition. All these recognition systems are discussed in this research paper. Biometric systems work on three levels: Enrollment, Verification, and Identification. Enrollment is the process in which patterns are captured from the user and stored in the database. Verification means to confirm that the sample entered by the user belongs to him or not. When the user wants to access the data then the user must use his/her biometrics so that the system checks that the person who wants to access the data is the real owner of the data or not. This process is identification. All three levels are the working levels of the Biometric System. In earlier years, biometrics were used only at ground levels to provide basic security to data but now the tables have turned. It is playing a major role in providing security to our data. Biometrics are not only used in day-to-day life in phone unlocking, phone assistants, attendance systems but also used at advanced levels like in airports, border security, cloud computing etc. In this research paper, we will discuss the future scope of biometric systems and how it could even change the future.
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Mudhafer Taher Al Mossawy, Mais, and Loay E. George. "A digital signature system based on hand geometry - Survey." Wasit Journal of Computer and Mathematics Science 1, no. 1 (April 1, 2022): 1–14. http://dx.doi.org/10.31185/wjcm.vol1.iss1.18.

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In recent years large number of emerging automated applications faces the need to have recognition abilities of persons using their own self biometrics, before they can access the applications services. Nowadays, Biometric recognition is used, it can be used as automatic identification or automatic verification of persons based on their physiological or behavioral characteristics. There are no perfect biometric measurements;each biometry has its advantages and limitations. Each biometry requires specific vital identity to answer the identification or verification question. The suitability of a particular biometry for a particular application depends on many factors. Hand geometry/shape is a very simple biometric technology that uses the measurements of human hand to verify the identity of the individuals. The measurements include the distance between certain mark points, shape and width of fingers and size of palm. The biometric systems that employing hand geometry become widely used since they have high public acceptance. This article aims to survey several articles found in literature about hand based biometric system, and to compare different methods of biometric recognition that based on hand geometry.
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Chinyemba, Melissa K., and Jackson Phiri. "Gaps in the Management and Use of Biometric Data: A Case of Zambian Public and Private Institutions." Zambia ICT Journal 2, no. 1 (June 29, 2018): 35–43. http://dx.doi.org/10.33260/zictjournal.v2i1.49.

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The current physical and cybersecurity systems rely on traditional three-factor authentication to mitigate the threats posed by insider attacks. Key is the use of biometric information. Biometrics are a unique measurement and analysis of the unique physiological special traits such as voice, eye structure and others that can be used in the discipline of varying person identification. Biometry, which is the analysis of these biometrics is a complex process but guarantees identification and non-repudiation. If used to identify humans then several issues such as where is the biometric data stored? Who has access to it? And how does one ensure that such data satisfies the principle of availability. To achieve availability, secure transportation arises. To achieve transportation, non-repudiation, confidentiality and authentication, integrity arise. A storage and transport system is recommended to these challenges. In this paper, we explore the gaps into how public and private institution store and manage biometrics information. We benchmarked each organization again the ISO 30107 and ISO 24745. Our results show that while most companies are adopting and using biometrics systems, few have adopted the ISO biometrics standards that govern the storage and management of biometric information and hence creating security risk.
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Kuznetsov, Denis. "Identification of a Personality." MATEC Web of Conferences 155 (2018): 01018. http://dx.doi.org/10.1051/matecconf/201815501018.

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Biometric technologies are based on biometrics, measurement of the unique characteristics of a certain person. These are the unique signs received by a person from birth and acquired characteristics that can change under the influence of time or external environment. This article discusses the principles of operation, types of biometric identification systems, describes examples and areas of their application.
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Omar, Bayan. "Fusion Method with Mean-discrete Algorithm in Feature level for Identical twins Identification." UHD Journal of Science and Technology 4, no. 2 (December 27, 2020): 141. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp141-150.

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The study on twins is an important form of study in the forensic and biometrics field as twins share similar genetic traits. A biometric is one of the common types of pattern recognition which acquires biometric data from a person. From these data, a feature is established and extracted where these features can be used to identify individual. Exiting works in biometric identification concentrate on unimodal biometric identification. The high similarity in a pair of twin’s biometric may lead to miss performance. Hence, due to their great accurateness, multimodal biometric systems have become more favored than unimodal biometric systems in identical twins identification. However, these systems are highly complex. We proposed Mean-Discrete feature based fusion algorithm for Kurdish handwriting and fingerprint for identical twins detection. Its viability and advantage over the unimodal biometric systems are highlighted. This paper employed 800 images from 50 pairs of identical twins from Kurdistan Region to carry out the experiment.
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Dissertations / Theses on the topic "Biometric identification systems"

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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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John, George Jacqueline. "Optimising multimodal fusion for biometric identification systems." Thesis, University of Kent, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418551.

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Verett, Marianna J. "Performance and usage of biometrics in a testbed environment for tactical purposes." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FVerett.pdf.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, December 2006.
Thesis Advisor(s): Alex Bordetsky. "December 2006." Includes bibliographical references (p. 71-74). Also available in print.
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Walnoha, Michael Anthony. "Shoeprint analysis a GIS application in forensic evidence /." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4639.

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Thesis (M.A.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains iv, 64 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 63-64).
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Dennis, LeRoy P. Ford Michael K. "The wireless ubiquitous surveillance testbed /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FDennis.pdf.

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Thesis (M.S. in Information Systems and Operations)--Naval Postgraduate School, March 2003.
Thesis advisor(s): Alex Bordetsky, Randy J. Hess. Includes bibliographical references (p. 101-105). Also available online.
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Johnson, Amos Y. Jr. "A method for human identification using static, activity-specific parameters." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/15793.

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Chan, Siu Man. "Improved speaker verification with discrimination power weighting /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20CHANS.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 86-93). Also available in electronic version. Access restricted to campus users.
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Crihalmeanu, Musat C. "Adding liveness detection to the hand geometry scanner." Morgantown, W. Va. : [West Virginia University Libraries], 2003. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3220.

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Thesis (M.S.)--West Virginia University, 2003
Title from document title page. Document formatted into pages; contains viii, 96 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 72-74).
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Feng, Yicheng. "Discriminability and security of binary template in face recognition systems." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1455.

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Akinbola, Akintunde A. "Estimation of image quality factors for face recognition." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4308.

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Thesis (M.S.)--West Virginia University, 2005.
Title from document title page. Document formatted into pages; contains vi, 56 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 52-56).
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Books on the topic "Biometric identification systems"

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Vacca, John R. Biometric technologies and verification systems. Amsterdam: Butterworth-Heinemann/Elsevier, 2007.

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James, Wayman, ed. Biometric systems: Technology, design and performance evaluation. London: Springer, 2005.

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Xiaoyuan, Jing, and Yang Jian, eds. Biometric image discrimination technologies. Hershey, PA: Idea Group Pub., 2006.

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Zhang, David. Automated biometrics: Technologies and systems. Boston: Kluwer Academic Publishers, 2000.

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Liu, Chengjun. Cross Disciplinary Biometric Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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W, Mak M., and Lin Shang-Hung 1968-, eds. Biometric authentication: A machine learning approach. Upper Saddle River, N.J: Prentice Hall Professional Technical Reference, 2005.

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Bodade, Rajesh M. Iris analysis for biometric recognition systems. New York: Springer, 2014.

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Gavrilova, Marina L. Multimodal biometrics and intelligent image processing for security systems. Hershey, PA: Information Science Reference, 2013.

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A, Simon Michael, ed. Biometrics for dummies. Hoboken, NJ: Wiley Pub., 2008.

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D, Saeed Khalid Ph, Pejaś Jerzy 1954-, and Mosdorf Romuald, eds. Biometrics, computer security systems and artificial intelligence applications. New York: Springer, 2006.

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Book chapters on the topic "Biometric identification systems"

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Gonzalez-Rodriguez, Joaquin, Javier Ortega-Garcia, and Jose-Luis Sanchez-Bote. "Forensic Identification Reporting Using Automatic Biometric Systems." In Biometric Solutions, 169–85. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1053-6_7.

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Smith, Marcus, and Seumas Miller. "The Future of Biometrics and Liberal Democracy." In Biometric Identification, Law and Ethics, 79–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90256-8_5.

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AbstractThe first part of this chapter considers future biometrics, with a focus on second generation biometrics that measure physiological patterns. The second discusses the potential biometric future – how the use of biometrics, data and algorithms more broadly, could be used by governments to regulate social and economic interactions. This discussion will draw on the development of credit systems, from those used in commercial online platforms to rate the performance of providers and users, to the more integrated and all-encompassing social credit system (SCS) implemented in China, as an example of a potential future development in liberal democratic countries. Finally, we discuss the key features of liberal democratic theory and how biometric and related technological developments may change governance in western democracies. While we briefly mention some relevant developments in the private sector, our main focus will be on the relationship between liberal democratic governments and their security agencies, on the one hand, and their citizenry, on the other. We describe in general terms how liberal democracies might respond to these new technologies in a manner that preserves their benefits without unduly compromising established liberal democratic institutions, principles and values. Accordingly, we seek to offer a response to some of the dual use ethical dilemmas posed by biometrics, albeit in general terms.
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Smith, Marcus, and Seumas Miller. "Biometric and Non-biometric Integration: Dual Use Dilemmas." In Biometric Identification, Law and Ethics, 57–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90256-8_4.

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AbstractBiometric identification is now closely integrated with other forms of data, data systems and communications technologies, such as smartphones, metadata and social media, and as the key security feature on smartphones, and by extension, social media accounts, online profiles and identity. For this reason, we consider the interaction between biometric and other forms of identification data, and data systems, building upon the consideration of the main biometrics in the first three chapters. We begin with a general discussion of data systems and integration. This is followed by a discussion of the interrelationship with biometrics, and broader significance of, metadata, smartphone applications and social media. In combination with biometric identification technologies, these provide detailed insights into individuals’ activities and behaviours. The ethical analysis in this chapter focuses on dual use dilemmas. Roughly speaking, dual use dilemmas in science and technology arise in virtue of the fact that such science and technology can be used to greatly benefit humankind, but also, unfortunately, to cause great harm to humankind. Consider, for instance, nuclear science and technology. It can be used as a cheap and peaceful energy source, or to build nuclear weapons. Similarly, facial recognition technology could be used by police only to track persons guilty of serious crimes; or it could be used to monitor ordinary citizens’ behaviour by an authoritarian government.
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Khari, Manju, Renu Dalal, Arti Sharma, and Bhavya Mehta. "Person Identification in UAV Shot Videos by Using Machine Learning." In Multimodal Biometric Systems, 45–60. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003138068-4.

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Nudzikova, Pavlina, and Zdenek Slanina. "User Identification by Biometric Methods." In Advances in Intelligent Systems and Computing, 181–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27644-1_17.

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Travieso, Carlos M., Juan C. Briceño, and Jesús B. Alonso. "Identification Approach Lip-Based Biometric." In Recent Advances in Intelligent Engineering Systems, 341–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23229-9_16.

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Adjedj, Michael, Julien Bringer, Hervé Chabanne, and Bruno Kindarji. "Biometric Identification over Encrypted Data Made Feasible." In Information Systems Security, 86–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10772-6_8.

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Chaudhary, Gopal, Smriti Srivastava, Saurabh Bhardwaj, and Shefali Srivastava. "Information Fusion in Animal Biometric Identification." In Advances in Intelligent Systems and Computing, 609–17. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3153-3_60.

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Bayat, Akram, and Marc Pomplun. "Biometric Identification Through Eye-Movement Patterns." In Advances in Intelligent Systems and Computing, 583–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60591-3_53.

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Gibbons, Michael, Sungsoo Yoon, Sung-Hyuk Cha, and Charles Tappert. "Evaluation of Biometric Identification in Open Systems." In Lecture Notes in Computer Science, 823–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527923_86.

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Conference papers on the topic "Biometric identification systems"

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Ahundzhanov, Umidzhon, and Valeriy Starovoytov. "PROBLEMS OF BIOMETRIC IDENTIFICATION IN ACCESS SYSTEMS." In CAD/EDA/SIMULATION IN MODERN ELECTRONICS 2021. Bryansk State Technical University, 2021. http://dx.doi.org/10.30987/conferencearticle_61c997ee310750.12735248.

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Handwritten signature recognition is a biometric method that can be used in many aspects of life when it is necessary to use personal signatures, for example, when cashing a check, signing a credit card, authenticating a document, etc. Innovative approaches to solving static signatures that have been introduced into the literature increase every year.
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Farafonova, A. E., and E. L. Turnetskaya. "TECHNOLOGIES OF BIOMETRIC PERSON’S IDENTIFICATION." In PROCESSING, TRANSMISSION AND PROTECTION OF INFORMATION IN COMPUTER SYSTEMS. St. Petersburg State University of Aerospace Instrumentation, 2020. http://dx.doi.org/10.31799/978-5-8088-1452-3-2020-1-43-49.

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Jaafar, Nur Azua Liyana, Khairul Azami Sidek, and Siti Nurfarah Ain Mohd Azam. "Acceleration plethysmogram based biometric identification." In 2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS). IEEE, 2015. http://dx.doi.org/10.1109/icbaps.2015.7292210.

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Li, Nan, Fuchun Guo, Yi Mu, Willy Susilo, and Surya Nepal. "Fuzzy Extractors for Biometric Identification." In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017. http://dx.doi.org/10.1109/icdcs.2017.107.

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Kwapisz, Jennifer R., Gary M. Weiss, and Samuel A. Moore. "Cell phone-based biometric identification." In 2010 IEEE Fourth International Conference On Biometrics: Theory, Applications And Systems (BTAS). IEEE, 2010. http://dx.doi.org/10.1109/btas.2010.5634532.

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Takeshi Yamakawa, Kazuhiko Taniguchi, Toshio Momen, Syoji Kobashi, Katsuya Kondo, and Yutaka Hata. "Biometric personal identification using sole information." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4414065.

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Khalifa, A., K. Sundaraj, Z. Ibrahim, and V. Retnasamy. "Complex background subtraction for biometric identification." In 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658477.

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Chudobova, Michaela, Jan Kubicek, Radomir Scurek, and Marek Hutter. "IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/2.1/s07.06.

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This article deals with the design and implementation of an intelligent biometric system that allows the detection and classification of a person's face from static image data and creates a system for evaluating its reliability. In its introductory part, it theoretically describes applied biometrics and biometric systems for security identification and user verification, and also deals with the theory of the description of algorithms for human face detection and recognition. Subsequently, the authors use the MATLAB programming language, which is highly optimized for modern processors and memory architectures, to focus on the implementation and testing of a biometric system using Viola-Jones algorithms and a convolutional neural network with a pre-trained network NetNet. Convolutional neural networks (CNN) are the most recognized and popular deep-learning neural networks, which are based on layers that perform two-dimensional (2D) convolution of input data with learned filters. In the final part there is a discussion where, based on the results of testing, the robustness and efficiency of the proposed intelligent biometric system is objectively evaluated. The results allow for the continued development of other pre-trained artificial neural networks, variable implementations for facial recognition, but also other things, such as the recognition of potentially dangerous people.
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Elsherief, Shimaa, Mahmoud Allam, and Mohamed Fakhr. "Biometric Personal Identification Based on Iris Recognition." In 2006 International Conference on Computer Engineering and Systems. IEEE, 2006. http://dx.doi.org/10.1109/icces.2006.320449.

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10

Diaz Alonso, Alain D., Carlos M. Travieso, Jesus B. Alonso, Malay Kishore Dutta, and Anushikha Singh. "Biometric personal identification system using biomedical sensors." In 2016 2nd International Conference on Communication, Control & Intelligent Systems (CCIS). IEEE, 2016. http://dx.doi.org/10.1109/ccintels.2016.7878210.

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Reports on the topic "Biometric identification systems"

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.
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Faith, Becky, and Tony Roberts. Managing the Risk and Benefits of Digital Technologies in Social Assistance Provision. Institute of Development Studies (IDS), February 2022. http://dx.doi.org/10.19088/basic.2022.025.

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Aid agencies, governments, and donors are expanding investment in the digitisation of their beneficiary identification and registration systems, in digitised systems for cash payments, and in the remote and algorithmic control of humanitarian and social protection programmes. This is happening in ways that may facilitate the move from humanitarian assistance to government provision and may enable the delivery of shock-responsive social protection. Yet humanitarian and social protection actors are increasingly concerned about a range of risks and accountability vacuums associated with the adoption of these technologies. While claims for the benefits of digitisation often rest on cost savings, data relating to these costs and benefits are not easily accessible. There is also an urgent need to adopt approaches to value for money in this sector that recognise the digital dignity of beneficiaries. A knowledge gap exists around how the movement towards biometric identification and algorithmic management using humanitarian and social protection data will affect the interests of vulnerable populations – so too does a gap in research that is focused on the standpoints, interests, and priorities of these populations.
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