An introduction to biometric recognition

By Anil K. Jain, Arun A. Ross, Salil Prabhakar. IEEE transactions on circuits and systems for video technology (Print), 2004

5,436 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2a16f050e68eb3e8700a7c5e0a40873dc87f45d7

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In Information Systems

In Signal Processing

Citations in the corpus

DCT-Based Iris Recognition (2007, background): “As security becomes an issue of importance, biometrics [3], [4], [5] and iris recognition in particular are attracting great interest [6].”
Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory (2009, neutral): “It uses a person’s physiological or behavioral characteristics such as ﬁngerprint, face, iris, gait, and signature for authentication [1].”
Biometrics: a tool for information security (2006, background): “Biometric authentication or, simply biometrics [5], [6], [69], refers to establishing identity based on the physical and behavioral characteristics (also”
Increase the Security of Multibiometric Systems by Incorporating a Spoofing Detection Algorithm in the Fusion Mechanism (2011, background): “The last property concerns the possibility of a non-client being falsely accepted, typically by spoofing the biometric trait of an authorized user [1].”
Behavioural biometrics: a survey and classification (2008, background): “The majority of research is aimed at studying well-established physical biometrics such as fingerprint (Cappelli et al,, 2006) or iris scans (Jain et al., 2004d).”
Biometric Gait Authentication Using Accelerometer Sensor (2006, methodology): “Biometric systems operate by acquiring biometric data from an individual, extracting feature set from the acquired data, and comparing this feature set against the enrolment set in a database [10].”
Feature Coding in Image Classification: A Comprehensive Study (2014, neutral): “human-computer interaction [4], and biometrics [5].”
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition (2007, background): “The human face is an easily collectible, universal, and nonintrusive biometric [19], which makes it ideal for applications in scenarios where fingerprinting or iris scanning are impractical (for example, surveillance) or undesirable due to problemsofsocial acceptance[20].”
Biometric Authentication and Identification using Keystroke Dynamics: A Survey (2012, background): “However, they provide sufficiently distinct information that can be used for identification and authentication [1].”
A Secure Biometrics-Based Multi-Server Authentication Protocol Using Smart Cards (2015, background): “In Table I, a comparison of various biometric technologies is provided based on seven factors [33].”
On the Feasibility of Internet-Scale Author Identification (2012, background): “Behavioral biometrics — voice [47], gait [48], handwriting [49], keystroke dynamics [50], etc.”
A survey of multilinear subspace learning for tensor data (2011, background): “Face recognition has a large number of commercial security and forensic applications, including video surveillance, access control, mugshot identification, and video communications [10, 91].”
A review of biometric technology along with trends and prospects (2014, background): “This is due to the fact that tattoos negate the basic requirement of “Universality” which clearly states that the attribute must be possessed by entire / majority of the population [1].”
A survey of biometric recognition methods (2004, background): “A brief comparison of the above techniques based on seven factors described in section 2 is provided in Table I [2].”
Robust Biometrics-Based Authentication Scheme for Multiserver Environment (2015, background): “If the two feature sets are matching, the system could recognize the individual; otherwise, the system will reject the individual [1]–[3].”
Multimodal fusion vulnerability to non-zero effort (spoof) imposters (2010, background): “The problem with any human trait that meets these criteria is in the performance, acceptability, and circumvention of the biometric trait [1].”
Combining match scores with liveness values in a fingerprint verification system (2012, methodology): “For each method the evaluation is conducted at two different levels: • Verification level: When distinguishing genuine from impostor pairs, the performance can be measured based on the errors made by a typical biometric system: False Match Rate (FMR) and False Non-Match Rate (FNMR) [5].”