Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences
on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time
between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris
images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson
University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on
iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of
iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris
recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek
algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris
recognition regression indicating the variability in match scores. According to the regression analysis, our study in this
paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility
to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.
Iris recognition has expanded from controlled settings to uncontrolled settings (on the move, from a distance)
where blur is more likely to be present in the images. More research is needed to quantify the impact of blur on iris
recognition. In this paper we study the effect of out-of-focus blur on iris recognition performance from images
captured with out-of-focus blur produced at acquisition. A key aspect to this study is that we are able to create a
range of blur based on changing focus of the camera during acquisition. We quantify the produced out-of-focus
blur based on the Laplacian of Gaussian operator and compare it to the gold standard of the modulation transfer
function (MTF) of a calibrated black/white chart. The sharpness measure uses an unsegmented iris images from a
video sequence with changing focus and offers a good approximation of the standard MTF. We examined the
effect of the 9 blur levels on iris recognition performance. Our results have shown that for moderately blurry
images (sharpness at least 50%) the drop in performance does not exceed 5% from the baseline (100% sharpness).
Low quality iris images such as blurry, low resolution images with poor illumination create a challenge
for iris recognition systems. Therefore, an efficient enhancement of iris images are needed in challenging
environments. We propose a new iris recognition algorithm for enhancement of normalized iris images. Our
algorithm is based on the logarithmic image processing (LIP) image enhancement which is used as one of the 3
stages in the enhancement process. Methods are tested on the MBGC database to compare enrolled video iris
images from 124 subjects with 220 pixels resolutions to query video portal images from 110 subjects with 120
pixels resolution. Results from processing challenging MBGC iris data show significant improvement in the
performance of iris recognition algorithms in terms of equal error rates compared to the original (unenhanced
images) and the other fast image enhancement methods.
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