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IPM
30
YEARS OLD

“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 11821
School of Cognitive Sciences
  Title:   Robust iris verification based on local and global variations
  Author(s): 
1.  Nima Tajbakhsh
2.  Babak Nadjararabi
3.  Hamid Soltanian zadeh
  Status:   Published
  Journal: Advances in Signal Processing
  Vol.:  2010
  Year:  2010
  Pages:   1-12
  Supported by:  IPM
  Abstract:
This work addresses the increasing demand for a sensitive and user-friendly iris based authentication system. We aim at reducing False Rejection Rate (FRR). The primary source of high FRR is the presence of degradation factors in iris texture. To reduce FRR, we propose a feature extraction method robust against such adverse factors. Founded on local and global variations of the texture, this method is designed to particularly cope with blurred and unfocused iris images. Global variations extract a general presentation of texture, while local yet soft variations encode texture details that are minimally reliant on the image quality. Discrete Cosine Transform and wavelet decomposition are used to capture the local and global variations. In the matching phase, a support vector machine fuses similarity values obtained from global and local features. The verification performance of the proposed method is examined and compared on CASIA Ver.1 and UBIRIS databases. Efficiency of the method contending with degraded images of the UBIRIS is corroborated by experimental results where a significant decrease in FRR is observed in comparison with other algorithms. The experiments on CASIA show that despite neglecting detailed texture information, our method still provides results comparable to those of recent methods.

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