Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 6,
  • Issue 11,
  • pp. 824-826
  • (2008)

Improving iris recognition performance via multi-instance fusion at the score level

Not Accessible

Your library or personal account may give you access

Abstract

Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multi-instance fusion, which combines the left and right irises of an individual at the matching score level. When fusing, a novel fusion strategy using minimax probability machine (MPM) is applied to generate a fused score for the final decision. The experimental results on CASIA and UBIRIS databases show that the proposed method can bring obvious performance improvement compared with the single-instance method. The comparison among different fusion strategies demonstrates the superiority of the fusion strategy based on MPM.

© 2008 Chinese Optics Letters

PDF Article
More Like This
Optimal wavelength band clustering for multispectral iris recognition

Yazhuo Gong, David Zhang, Pengfei Shi, and Jingqi Yan
Appl. Opt. 51(19) 4275-4284 (2012)

Double random phase encoding for cancelable face and iris recognition

Randa F. Soliman, Ghada M. El Banby, Abeer D. Algarni, Mohamed Elsheikh, Naglaa F. Soliman, Mohamed Amin, and Fathi E. Abd El-Samie
Appl. Opt. 57(35) 10305-10316 (2018)

Extending the imaging volume for biometric iris recognition

Ramkumar Narayanswamy, Gregory E. Johnson, Paulo E. X. Silveira, and Hans B. Wach
Appl. Opt. 44(5) 701-712 (2005)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.