A robust eyelash detection based on iris focus assessment

Byung Jun Kang, Kang Ryoung Park

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

For accurate iris recognition, it is essential to detect eyelash regions and remove them for iris code generation, since eyelashes act as noise factors in the iris recognition. In addition, eyelash positions can be changed for enrollment and recognition and this may cause FR (false rejection). To overcome these problems, we propose a new method for detecting eyelashes in this paper. This work shows three advantages over previous works. First, because eyelash detection was performed based on focus assessment, its performance was not affected by image blurring. Second, the new focus assessment method is appropriate for iris images. Third, the detected eyelash regions were not used for iris code generation and therefore iris recognition accuracy was greatly enhanced. Experimental results showed that the eyelash detection error was about 0.96% when using the CASIA DB and iris recognition accuracy with eyelash detection was enhanced more than 0.86% of EER when compared to the EER obtained without eyelash detection.

Original languageEnglish
Pages (from-to)1630-1639
Number of pages10
JournalPattern Recognition Letters
Volume28
Issue number13
DOIs
StatePublished - 1 Oct 2007

Keywords

  • Eyelash detection
  • Iris recognition

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