Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems-Reference-Cited by-同舟云学术

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

Published:2018-03-16 Issue:5 Volume:7 Page:474-481
ISSN:2047-4938
Container-title:IET Biometrics
language:en
Short-container-title:IET biom.

Author:

Sharma Renu¹²,Das Sukhendu³,Joshi Padmaja¹

Affiliation:

1. Centre for Development of Advanced ComputingMumbaiIndia

2. Computer Science and EngineeringMichigan State UniversityMIUSA

3. Department of Computer Science & EngineeringIndian Institute of Technology MadrasChennaiIndia

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2017.0076

Reference54 articles.

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2. Li Z. Vinyals O. Baker H. et al. : ‘Feature learning using generalized extreme value distribution based k‐means clustering’.Int. Conf. Pattern Recognition 2012 pp.1538–1541

3. Fragoso V. Sen P. Rodriguez S. et al. : ‘EVSAC: accelerating hypotheses generation by modeling matching scores with extreme value theory’.IEEE Int. Conf. Computer Vision 2013 pp.2472–2479

4. Gibert X. Patel V.M. Chellappa R.: ‘Sequential score adaptation with extreme value theory for robust railway track inspection’.IEEE Int. Conf. Computer Vision Workshops 2015 pp.131–138

5. NIST: ‘Biometric scores set (BSSR1)’. Available athttps://www.nist.gov/itl/iad/image-group/nist-biometric-scores-set-bssr1 2004

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