12 research outputs found
Quality parameter assessment on iris images
Iris biometric for personal identification is based on capturing an eye image and obtaining
features that will help in identifying a human being. However, captured images may not
be of good quality due to variety of reasons e.g. occlusion, blurred images etc. Thus, it is
important to assess image quality before applying feature extraction algorithm in order to
avoid insufficient results. Poor quality images may affect the recognition as they have few
sufficient feature information. Moreover, existing quality measures focuses on parameters
or factors than feature information. In this paper, iris quality assessment research is
extended by analysing the effect of entropy, contrast, area ratio, occlusion, blur, dilation
and sharpness of an iris image which determines the iris size, amount of information and
clearness of the features. A weighting method based on principal component analysis
(PCA) is proposed to determine the influence each parameter has on the quality score. To
test the proposed technique; Chinese Academy of Science Institute of Automation (CASIA),
Internal Collection (IC) and University of Beira Interior (UBIRIS) databases are
used. A conclusion is drawn that the combination of blur, dilation and sharpness parameters
have the most influence in the quality of the image as they weighed more than other
parameter