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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Hand geometry based recognition with a MLP classifier
Marcos Faundez-Zanuy,Miguel Angel Ferrer-Ballester,Carlos M. Travieso-González,Virginia Espinosa-Duro +3 more
TL;DR: In this article, a biometric recognition system based on hand geometry is presented, which consists of 50 people and 10 different acquisitions of the right hand, which can be freely downloaded.
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“Are we getting the biometric bioethics right?” – the use of biometrics within the healthcare system in Malawi
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TL;DR: Improved community engagement and sensitization should be a required component of biometrics introduction in Malawi because of the range of potential ethical issues and practical challenges that impact on equitable uptake.
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Design and Evaluation of an Authentication Framework for Wearable Devices
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Online writer identification using statistical modeling-based feature embedding
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TL;DR: A novel framework based totally on identity vector is introduced for the online writer identification task and can achieve an improvement over the best reported result of the current state-of-the-art approaches with the exception of fully end-to-end approaches.
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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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