An automatic iris occlusion estimation method based on high-dimensional density estimation

Yung Hui Li, Marios Savvides

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.

Original languageEnglish
Article number6261319
Pages (from-to)784-796
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume35
Issue number4
DOIs
StatePublished - 2013

Keywords

  • Gaussian mixture models
  • biometrics recognition
  • iris mask
  • iris occlusion estimation
  • iris recognition
  • simulated annealing

Fingerprint

Dive into the research topics of 'An automatic iris occlusion estimation method based on high-dimensional density estimation'. Together they form a unique fingerprint.

Cite this