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
CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system
TL;DR: A new frame analysis mechanism, continuous frames skipping mechanism (CFSM), which can analyze the frame in real time to determine whether it is necessary to perform face recognition on the current frame, achieving the goal of real-time face recognition in the embedded system.
4
•Dissertation
Application-driven Advances in Multi-biometric Fusion
Naser Damer
- 01 Jan 2018
TL;DR: A novel performance anchored score normalization technique is presented that aligns certain performance-related score values in the fused biometric sources leading to more accurate multi-biometric decisions when compared to conventional normalization approaches.
4
•Dissertation
Continuous Biometric Identification on the Steering Wheel
Joao Ribeiro Pinto
- 24 Jul 2017
TL;DR: It was found that a succession of fivesecond signal segmentation, denoising with Savitzky-Golay and a Moving Average Filter, heartbeat segmentation after Trahanias R-peak detection, and outlier removal based on cross-correlation clustering, was able to significantly clean the signal.
4
Assessment of age changes and gender differences based on anthropometric measurements of ear: A cross-sectional study
TL;DR: The anthropometric measurements of ear index were higher in men than women, with no age changes after 20 years of age for both men and women, and this data are considered reliable and can be used for various purpose including forensics, identification, plastic surgeries, and research.
4
Biometric Authentication Based on Pupillary Light Reflex Using Neural Networks
Vitor Yano,Lee Luan Ling,Alessandro Zimmer +2 more
- 26 Jun 2013
TL;DR: This paper presents a proposal for the use of the features of the Pupillary Light Reflex for user authentication, using artificial neural networks for classification.
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.
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