Recognition Oriented Iris Image Quality Assessment in the Feature Space

  title={Recognition Oriented Iris Image Quality Assessment in the Feature Space},
  author={Leyuan Wang and Kunbo Zhang and Min Ren and Yunlong Wang and Zhenan Sun},
  journal={2020 IEEE International Joint Conference on Biometrics (IJCB)},
A large portion of iris images captured in real world scenarios are poor quality due to the uncontrolled environment and the non-cooperative subject. To ensure that the recognition algorithm is not affected by low-quality images, traditional hand-crafted factors based methods discard most images, which will cause system timeout and disrupt user experience. In this paper, we propose a recognition-oriented quality metric and assessment method for iris image to deal with the problem. The method… 

Figures and Tables from this paper


Comprehensive assessment of iris image quality
Extensive experiments demonstrate the proposed framework can effectively assess the overall quality of iris images and the relationship between iris recognition results and the quality level of iri images can be explicitly formulated.
Realtime Quality Assessment of Iris Biometrics Under Visible Light
A fast no-reference image quality assessment measure for predicting iris image quality to handle severely degraded iris images and improved the performance of the iris recognition system by rejecting poor quality iris samples.
Global and local quality measures for NIR iris video
  • Jinyu Zuo, N. Schmid
  • Computer Science
    2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
  • 2009
New global and local factors that can be used to evaluate iris video and image quality are introduced and a fast global quality evaluation procedure for selecting the best frames from a video or an image sequence is introduced.
Image quality assessment for iris biometric
It is concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most and a fully automated iris image quality evaluation block is designed that operates in two steps.
A Selective Feature Information Approach for Iris Image-Quality Measure
  • C. Belcher, Yingzi Du
  • Computer Science
    IEEE Transactions on Information Forensics and Security
  • 2008
The experimental results show that the proposed quality score is highly correlated with the recognition accuracy and is capable of predicting the recognition results.
Iris quality assessment and bi-orthogonal wavelet based encoding for recognition
Estimating and Fusing Quality Factors for Iris Biometric Images
This paper designs a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, off-angle, occlusion, lighting, specular reflection, and pixel counts and fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning.
Quality Assessment of Degraded Iris Images Acquired in the Visible Wavelength
  • Hugo Proença
  • Computer Science
    IEEE Transactions on Information Forensics and Security
  • 2011
A method to assess the quality of VW iris samples captured in unconstrained conditions, according to the factors that are known to determine thequality of iris biometric data, which permits us to handle severely degraded samples that are likely to result of such imaging setup.
Robust and Fast Assessment of Iris Image Quality
A novel approach for iris image quality assessment is proposed that uses three distinctive features to distinguish three kinds of poor quality images, i.e. defocus, motion blur and occlusion.