Proceedings Article10.1109/ICPR.2008.4760935
An introduction to biometrics
Anil K. Jain,Arun Ross,Karthik Nandakumar +2 more
- 01 Dec 2008
- pp 1-1
717
TL;DR: In this paper, the design of a biometric system is discussed from the viewpoint of four commonly used biometric modalities -fingerprint, face, hand, and iris.
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Abstract: Summary form only given. Biometrics refers to the automatic identification (or verification) of an individual (or a claimed identity) by using certain physical or behavioral traits associated with the person. By using biometrics it is possible to establish an identity based on `who you are?, rather than by `what you possess? (e.g., an ID card) or `what you remember? (e.g., a password). Therefore, biometric systems use fingerprints, hand geometry, iris, retina, face, vasculature patterns, signature, gait, palmprint, or voiceprint to determine a person?s identity. The purpose of this tutorial is two-fold: (a) to introduce the fundamentals of biometric technology from a pattern recognition and signal processing perspective by discussing some of the prominent techniques used in the field; and (b) to convey the recent advances made in this field especially in the context of security, privacy and forensics. To this end, the design of a biometric system will be discussed from the viewpoint of four commonly used biometric modalities - fingerprint, face, hand, and iris. Various algorithms that have been developed for processing these modalities will be presented. Methods to protect the biometric templates of enrolled users will also be outlined. In particular, the possibility of performing biometric matching in the cryptographic domain will be discussed. The tutorial will also introduce concepts in biometric fusion (i.e., multibiometrics) in which multiple sources of biometric information are consolidated. Finally, there will be a discussion on some of the challenges encountered by biometric systems when operating in a real-world environment and some of the methods used to address these challenges.
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Citations
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Automatic signature and graphical password verification: Discriminant features and new application scenarios
Marcos Martínez Díaz
- 01 Feb 2015
TL;DR: Tesis doctoral inedita leida en la Escuela Politecnica Superior, Departamento de Tecnologia Electronica y de las Comunicaciones.
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Application of hyperspectral imaging in hand biometrics
Agnieszka Jenerowicz,Piotr Walczykowski,Lukasz Gladysz,Mateusz Gralewicz +3 more
- 08 Oct 2018
TL;DR: The hyperspectral data are used to develop the methodology of fast imagery post-processing and data matching, which could be used to developed a low-cost hand biometric recognition system intended to make a highly efficient identification and to have a fast response and easy usage.
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Robust enhancement and centroid-based concealment of fingerprint biometric data into audio signals
Sani M. Abdullahi,Hongxia Wang +1 more
TL;DR: An 8-layered feature enhancement algorithm is proposed and the fingerprint image was enhanced and extracted using minutiae-based recognition system with the aim of eliminating all anomalies that comes with the image.
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Orisyncrasy—An Ear Biometrics on the Fly Using Gabor Filter
Labhesh Valechha,Hitesh Valecha,Varkha Ahuja,Tarun Chawla,Sharmila Sengupta +4 more
- 01 Jan 2020
TL;DR: EarBiometric system can capture the ear from a distance even without the knowledge of the subject under test as it is a passive biometric system and is invariant to rotation of profile face in same or different planes.
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Criminal Proceedings in Cyberspace: The Challenge of Digital Era
Vanja Bajović
- 01 Jan 2017
TL;DR: The author explains non-applicability of traditional criminal law and procedure in cyberspace and advocates the introduction of biometric identification security systems, online reporting centres, universal cyber police and “two track” criminal proceedings for cybercrimes.
4
References
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
An introduction to biometric recognition
TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
High confidence visual recognition of persons by a test of statistical independence
TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
How iris recognition works
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
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