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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
EER Calculation and DET Approximation in a Multi-Threshold Biometric System
Juan Arteaga-Falconi,Diana P. Tobon,Abdulmotaleb El Saddik +2 more
- 01 Jun 2018
TL;DR: A method to get the Detection Error Trade-off—DET—graph when two or more thresholds are involved in a biometric system and a method to calculate the Equal Error Rate—EER—in a multi-threshold system is provided.
2
Case Study: Interpretability of Fuzzy Systems Applied to Identity Verification
Krzysztof Cpałka
- 01 Jan 2017
TL;DR: Typical schemes of fuzzy system learning are presented, which can be used, for example, in the case when the authors do not have expert knowledge but they have learning data, and in which an expert initially determines the number of rules and approximate distribution of fuzzy sets.
2
A standoff multimodal biometric system
Chris Bensing Boehnen,Christopher J. Mann,Dilip R. Patlolla,D. Barstow +3 more
- 01 Nov 2011
TL;DR: This work describes the design of a prototype standoff biometric sensor that captures face and iris images at a lower frame rate, lower resolution, and wider field of view than existing standoff iris sensors.
2
•Proceedings Article
A Socio-Technical Approach to Biometric Technology Deployment in Schools
Rachida Parks,Esther Mead +1 more
- 01 Jan 2014
TL;DR: Applying a socio-technical approach, a clear understanding is gained between the technology of biometrics and its social environment and technical and social implications that include security advantages and concerns, privacy concerns, awareness issues, and legal considerations are identified.
2
Biometrics for Global Web Authentication: an Open Source Java/J2EE-Based Approach
TL;DR: Over the last couple of years, the team has been working on using biometric based integration to advance the security model of the existing J2EE/Java EE based platform and to provide solution to the open source community.
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.
3K
Related Papers (5)
Shaleen Bhatnagar,Nidhi Mishra +1 more
- 15 May 2020
Sheikh Imroza Manzoor,Arvind Selwal +1 more
- 01 Dec 2018
A S Raju,V. Udayashankara +1 more
- 01 Nov 2014