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
Neurogenetic reconstruction of biometric templates: A new security threat?
Joshua Adams,Gerry Dozier,Kelvin Bryant,Joseph Shelton,Aniesha Alford,Derrick Leflore,Tamirat Abegaz +6 more
- 15 Mar 2012
TL;DR: This paper demonstrates how neurogenetic reconstruction can be used to reconstruct facial images from biometric templates extracted using Local Binary Patterns (LBPs) and shows that reconstructed images can beused to recognize individuals within a dataset with a high degree of accuracy.
4
Design of new P2P-enabled Mobile-OTP system using fingerprint features
TL;DR: Based on a set of simulations for the proposed random password key generation method, the efficiency and strength of proposed modification for Mobile-OTP systems are evaluated and application scenarios to support secure service are described.
4
A Feature-Level Fusion Scheme Based on Eigen Theory for Multimodal Biometrics
TL;DR: A novel multimodal biometric recognition with information fusion at feature level, called eigen-based recognition, is presented, which aims to gain the reliability for biometrics.
4
An interpretable fuzzy system in the on-line signature scalable verification
Marcin Zalasiński,Krzysztof Cpałka,Krystian Lapa +2 more
- 01 Jul 2020
TL;DR: This paper proposes new original solutions for the use of interpretable flexible fuzzy systems for identity verification based on an on-line signature that meets all of the above requirements and works effectively for the on- line signatures' database used in the simulations.
4
Using color histogram as the trait of retina biometric
Hao Hao,Dinesh Kumar,Behzad Aliahmad,Mohd Zulfaezal Che Azemin,Ryo Kawasaki +4 more
- 28 Mar 2013
TL;DR: A method using color histogram as the trait of retina biometric, which minimizes the difference within the individuals and increases the correlation between the retinal images of the same person.
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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