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
A Multi-sensory Gesture-Based Login Environment
Ahmad Qamar,Abdullah Murad,Md. Mohamed Rahman,Faizan Ur Rehman,Akhlaq Ahmad,Bilal Sadiq,Saleh Basalamah +6 more
- 13 Oct 2015
TL;DR: A multi-sensory gesture based login system that allows a user to access secure information using body gestures and is scalable enough to support sensors that detect a large number of gestures to those that can only accept a few.
3
Engineered and learned features for face and facial expression recognition
S. Elaiwat
- 01 Sep 2015
TL;DR: Novel feature extraction methods based on hand-engineered global and local features geared towards the problem of face recognition in still images are presented and a novel Curvelet local feature approach is proposed to extract local features rather than global features.
Cancelable Fingerprint Recognition based on Encrypted Convolution Kernel in Different Domains
Fatma G. Hashad,Osama Zahran,Sayed El-Rabaie,Ibrahim F. Elashry,Ghada M. El-Banby,Moawad I. Dessouky,Fathi E. Abd El-Samie +6 more
- 01 Jul 2020
TL;DR: A comparative study between different transform-domains in the occurrence of attacks shows the authority of encryption in the DWT domain with different keys.
3
Multimodal of face and iris based on local binary pattern and gabor-zernike moments
Akram Alsubari,R. J. Ramteke +1 more
TL;DR: This paper presents the multimodal of face and iris based on the same feature extraction techniques for the both traits, and the performance of the system is found to be satisfactory.
3
BPN Based Likelihood Ratio Score Fusion for Audio-Visual Speaker Identification in Response to Noise
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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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