Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density

Niladri B. Puhan, N. Sudha, Anirudh Sivaraman Kaushalram

Research output: Contribution to journalArticlepeer-review


In a less constrained capture of iris images to build a high-speed iris recognition system, the design of a robust and fast iris segmentation method is important. In this paper, a new iris segmentation technique based on the Fourier spectral density is proposed for noisy frontal view eye images captured with minimum cooperation from the subjects. The proposed segmentation method is not an iterative technique and it runs in deterministic time. The computational complexity of the proposed method is found to be significantly lower than the existing approaches based on integro-differential operator, Hough transform and active contour. The basic idea underlying the proposed method is to localize the limbic and pupil boundaries using the Fourier spectral density. The performance studies on a recently created iris database, called UBIRIS (Proenca and Alexandre in Lect Notes Comput Sci 3617:970-977, 2005) containing defocused, reflection-contained and eyelid-occluded iris images in visible spectral range, show that the proposed method is much faster than the existing methods and simultaneously achieves good segmentation accuracy.

Original languageEnglish (US)
Pages (from-to)105-119
Number of pages15
JournalSignal, Image and Video Processing
Issue number1
StatePublished - Mar 2011


  • Fourier spectral density
  • Image
  • Iris recognition
  • Low complexity
  • Segmentation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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